Psychology of Decision Making & Ergonomics Review PDF

PSYCHOLOGY OF DECISION MAKING & ERGONOMICS REVIEW THE CONCEPT OF WELLBEING WHAT IS WELLBEING? Despite the diversity of context in which wellbeing appears, there is consistency in the qualities it promises: Positive change: In contrast to many policy approa...

PSYCHOLOGY OF DECISION MAKING & ERGONOMICS REVIEW THE CONCEPT OF WELLBEING WHAT IS WELLBEING? Despite the diversity of context in which wellbeing appears, there is consistency in the qualities it promises: Positive change: In contrast to many policy approaches which have taken negatives as their focuses (poverty, social exclusion etc); may seem like a trivial difference however its a step to avoid social stigma; Something that is more constructive compared to focusing on the negative sides When you speak about the negative sides (EX. I want to stop the war/ resolve poverty) you are identifying a group of people & promoting some stigma surrounding them - EX “I want to stop the war”: If you focus on the negative side, you are going to limit the problem to a specific group of people, instead if you say “I want to promote peace” you include everyone, since it is not a problem limited to a group Holistic outlook: Rejection of the compartmentalization of people’s lives; Allows to make a global effort that is not limited to a specific objective; people that sustain this kind of concept think that it is better to achieve this positive change in people Centered in the person: Not only to the external “objective” measure of welfare but also the people’s own perceptions (wellbeing can be subjective to different cultures); contrasted to the objective indicators of other measures (income, nutrition, life expectancy etc.); Very often we pay attention to an objective measure of welfare; but we also have to think about people’s own perception of life; looking just at an objective measure doesn’t necessarily communicate if there is a high level of wellbeing or not; the idea is to deepen the subjective experience of people Goodhart's Law (1975): When a measure becomes a target, it ceases to be a good measure - Biagioli (2016): Goodhart’s Law states that when a feature of the economy is picked as an indicator of the economy, then it ceases to function as that indicator because people start to game it The concept of wellbeing is difficult to define precisely; how people understand wellbeing is different depending on the context - Crisp (2017): What is intrinsically valuable is relative to someone; what is ultimately good for this person - Doing well, feeling good - Doing well: Material dimension of welfare; suggests a foundation in economic prosperity - Feeling good: Subjective dimension of personal perceptions - Relationship dimension: Grounded in the sociocultural locations as well as the quality of relationships DEFINITION: LIMITATIONS: 1. Wellbeing is a concept for rich people; poor people have other, more immediate concerns to deal with 2. Wellbeing within policy & politics; if “subjective wellbeing” is a widely accepted concept, how is it supposed to be assessed? - EX. If a happiness assessment finds that people in a poor country are “happier” than many in wealthier countries, does this undermine the case for international aid? 3. Individualism; Dominant theories & measures of psychological well being are grounded in the western cultural values of liberal individualism; models of psychological well being conform to the dominant ideology of western society (Christopher, 1999) MORE COMPLEXITY: Different specific forms of wellbeing (mental, physical, economic etc) are often closely interlinked - EX. improved physical wellbeing is associated with improved emotional wellbeing The subjective differences between wellbeing & pleasure and wellbeing & happiness must be clearly distinguished - Usually referred to as synonyms - Pleasure: An experience that feels good - Happiness: The individual’s balance of pleasant over unpleasant experiences or state of being satisfied with one’s life as a whole (Haybron, 2020) THE CLASSIC TRIPARTITE MODEL OF DIENER: Diener’s Tripartite Model of Subjective Well-Being (1984): One of the most comprehensive models of well-being in psychology; three distinct but related components of wellbeing: - Frequent positive affect - Infrequent negative affect - Cognitive evaluations such as life satisfaction POSITIVE PSYCHOLOGY APPROACH : Central factors: - Having positive emotions - being engaged in an activity - having good relationships - finding meaning in one’s life - a sense of accomplishment (Saligman, 2011) WORKING DEFINITION: Feeling good & doing well (subjective, material, relationship) Who decides? →It is assumed that there is a decision-maker (an institution that promotes policies) that takes responsibility for promoting individual & collective well-being (paternalism) Paternalism: It tries to influence choices in a way that will make people better off, as judged by themselves; favors promoting positive choices for the individual & the community - Human judgment & choice; the debate about rationality - Ergonomics; interactions among human & other elements of a system  DISCOVERING, ACQUIRING, COMBINING INFORMATION, FEEDBACK BRUNSWIK’S LENS MODEL: - Psychology should focus both on the organism & the properties of the environment - The environment with which the organism interacts is uncertain and probabilistic (RANDOM). - Adaptation to a probabilistic world requires that the organism learns to employ probabilistic means to achieve goals & learns to use probabilistic & uncertain evidence about the world - Emphasis on Ecological Validity: the degree to which the cues reflect the criterion ↑ the Brunswik’s Lens Model fits within this framework Brunswik Lens Model: A conceptual framework for understanding human decision-making particularly in environments with uncertainty - This model uses a “lens” analogy to describe how people perceive & interpret various cues in their environment to make decisions - Highlights how humans rely on multiple cues or pieces of information when making judgements, even if those cues are incomplete - Often illustrated as a lens-shaped diagram where cues from the environment are processed through two parallel lenses - One representing how accurately the environment provides cues - One representing how well the individual uses those cues The goal is to align these two processes to maximize decision-making accuracy. Cue: Refers to any piece of information or signal in the environment that can help a person make a decision - A key insight of the BLM is that individuals often over or under- weigh certain cues leading to errors in judgment The model emphasizes that decision-making is inherently probabilistic; there is rarely one “right” decision but rather a range of possibilities informed by how effectively cues are processed - Decisions are made based on the probability that certain cues accurately represent reality, rather than on absolute certainty - In real-world decision making, the cues available to use are often incomplete or ambiguous; the model recognizes that people cannot always make decisions with 100% accuracy due to this uncertainty - Decision-makers are constantly balancing probabilities based on available cues & making decisions based on which outcomes seem most likely → stands in contrast to deterministic models where a certain input always leads to a specific output; in the BLM, the “best” decision is not guaranteed to be correct, it's just the MOST LIKELY correct decision based on the evidence at hand LENS MODEL EQUATION I am interested in the equation that tells me the correlation between the true values & the criterion because usually the true values are not available to me; the results show the correlation between the true values & the criterion & it is very simple if the errors correlation is 0 BRUNSWIK’S LENS MODEL: OBSERVATIONS The Rational Approach makes the strong assumption that people’s judgements would be linear mathematically → it's a simplification; it's easy to find situations in which the Rational Approach/Brunswik’s Lens Model doesn't work This kind of approach is contrasted often with Bounded Rationality Approach (says that the human mind tries to simplify situations) Two classical models have dominated research on judgment & decision-making: 1. Rational Approaches: Assumes that individuals can integrate all available information, combing & weighting it as if using an algebraic model; psychology should focus BOTH on the ORGANISM & the properties of the ENVIRONMENT 2. Bounded Rationality Approaches: Explicitly acknowledges the limits of information processing & people are modeled as if they use Heuristics KEY ELEMENTS - Discovering Information: How do we know what to look for; it is not so trivial - Acquiring/Researching Information: How much information do we need to gather? In what order? Should we evaluate one aspect before evaluating another - EX: When you look at an individual, to understand if they're rich, do you look at the clothes or the jewelry first? - Combining the Information: How should the information be combined to form an overall judgment? - Feedback: Once the judgment has been made, how do we use the information regarding the discrepancy between our estimate & the reality to revise our beliefs? - Our beliefs are always revised due to the lack of evidence you had at first; you start to think that if you have limitation in judging people based off of a couple of objects, you may be wrong DISCOVERING INFORMATION When people need to make a decision or formulate a judgment based on multiple pieces of information, the whole trick is to decide what variables to focus on and then how to weigh or combine them to arrive at the best outcome. ACQUIRING INFORMATION Knowing where to look, the problem becomes: - WHAT DO I LOOK AT? - HOW MUCH DO I LOOK? - WHEN DO I STOP? Two approaches: - Rational: formal theories of optimal information search that require very complex calculations; the problem → there are many ways to calculate the informational value, plus do people actually use these criteria? - Bounded rationality: simplified strategies/heuristics of various kinds Many approaches conceptualize the problem of information acquisition/search in terms of a trade-off between exploitation & exploration - Exploitation: sticking with the standard option; making the most of the point one is at (EX. seeing old long-time friends) - Exploration: changing the option from the standard (EX. seeing new friends) Structured, Uncertainty-Driven Exploration in Real-World Consumer Choice: Study found that people behaved in a rational way when consuming; shifting between Exploration & Exploitation in a rational way This is an example of the dynamics between Exploration & Exploitation → you try to do something that is new, but if it doesn't work then you return on your default choice (a sure choice); trying something new brings some risks, but in certain cases it can be rewarding - Graph A: When you find something that you like, it is probable that next time you will most likely pick that thing - Graph B: With more experience in something, people tend to stick to what they like - Graph C: Exploration comes at a cost - Graph D: People are able to learn from errors Every behavior related to acquiring/searching for information falls on the exploration-exploitation criterion, an important factor is how to prioritize the various pieces of information/alternatives Payne (1979) Example: We need to rent a house & have Available Information and the various Alternatives → two modalities - Alternative-Wise VS Attribute-Wise Alternative-wise means all attributes of each alternative are assessed to obtain an overall evaluation, they are then compared and the choice is made. Typically, all the information is used and there are many algorithms available to perform the calculations and comparisons Attribute -wise involves selecting a limited number of attributes and comparing the alternatives based on these selected attributes, usually through a heuristic; typically not all information is used Payne, Bettman & Johnson (1993): Propose a framework for evaluating the strategies of a decision-maker; simple idea of trade-off between accuracy of the decision & the effort → when I make a decision there is a trade off between my accuracy & the effort I must put in to be accurate - The tradeoff comes from people wanting to be as accurate as possible, but not putting in a lot of effort; effortless strategies can cause for non-accurate decisions - There are several types of strategies; when we choose a strategy, we can locate our strategies in this kind of dimensional space (accuracy VS effort) Usually, alternative-wise strategies are rational, effortful and accurate, whereas attribute-wise strategies are heuristics-based, bounded rationality-based and thus less accurate but more convenient from a cognitive effort POV In the case of unlimited time and willingness to use significant cognitive resources, an alternative-wise strategy can be employed; each alternative in the set available is considered and for each one, an algorithmic evaluation is made by assigning weights to each attribute COMBINING INFORMATION After the search for information is complete, there are two strategies to combine these pieces of information: - Compensatory: Relies on all the information, both positive & negative (which balances out), within the same option; information is “weighted”; these strategies are typically associated with Alternative-Wise strategies - Non-Compensatory: Uses only part of the information & eliminate alternatives that do not meet certain criterion; these strategies are typically associated with Attribute-Wise strategies COMBINING INFORMATION: COMPENSATORY STRATEGIES Compensatory Strategies typically use a weighted linear rule (like Brunswik’s Lens Model) - First, assign a weight (𝑤𝑗)from 0 (no importance) to 10 (maximum importance) to each attribute - For each attribute of every option, a value could be (𝑎𝑖𝑗) +1 is present, -1 is not present; there can be many metrics for the attributes How effective are Compensatory Strategies? - Meehl (1954): Weighted linear model performs better then experts - Grove et. al (2000): Meta analysis found that algorithms perform 10% better than experts  - Kaufmann & Wittman (2016): Meta-analysis suggests human judgment should be replaced by automated, statistical judgment Why does this happen? - Algorithms always produce the same result for each set of data, whereas expert judgment can vary depending on the individual for many reasons; humans use limited samples to draw conclusions & sometimes generalize even when it is not warranted What is the role of humans? - Einhorn (1972): Although experts have difficulty combining information compared to algorithms, they are still good at using individual components; experts perform well in identifying the necessary components for accurate judgements but have limitations in combining them - Note: it is not the assignment of weights that is crucial, but the correct selection of variables & their signs (positive/negative) Dawes Rule (Dawes, 1979): Showed that by selecting the correct variables & signs, any linear model, regardless of the value of the weights, performs better than human experts COMBINING INFORMATION: NON-COMPENSATORY STRATEGIES It is not always possible to use a Compensatory Strategy; how good are humans’ decision-making skills when relying on smaller amounts of information? - Gigerenzer (1999/2011): Approach which discusses fast & frugal heuristics that guide human behavior/ decision-making based on ignorance; the human mind has a “adaptive toolbox” consisting of simple heuristics that help them to solve various problems (choosing between alternatives, categorizing objects, estimating quantities etc.) Gigerenzer’s Adaptive Toolbox: - Recognition Heuristic: If one of two alternatives is recognized, infer that it has the higher value on the criterion; when people have to make a choice in a quick manner, they prefer that they use something that they recognize/is familiar - Fluency Heuristic: If one alternative is recognized faster than another, infer that it has the higher value on the criterion; EX. Alberto VS Alessandro Manzoni - Take the Best: Infer which of the two alternatives has the higher value by: - Searching through cues in order of validity - Stopping the search as soon as a cue discriminates - Choosing the alternatives this cue favors - Tallying: To estimate a criterion, do not estimate weights but simply count the number of favoring cues; counting the number of the cues - Satisficing: Search through alternatives & choose the first one that exceeds your aspiration level - 1/N; Equality Heuristic: Allocate resources equally to each of N alternatives - Default: If there is a default, do nothing about it; usually consumers accept the default setting of product - Tit-for-Tat: Cooperate first & then imitate your partner’s last behavior; used in a game context; at the beginning cooperate, but then do whatever moves they do - Imitate the Majority: Look at majority of people in your peer group & imitate their behavior - Imitate the Successful: Look for the most successful person & imitate their behavior Instead of doing the mathematical calculations, heuristics allow us to reach very good outcomes more quickly Take-the-best (TTB) heuristic is one the most studied/relevant in non-compensatory strategies; it is a heuristic for binary choices TTB is based on two principles: 1. Recognition principle: in a decision under uncertainty, if one alternative is recognized and the other is not, then the recognized alternative is chosen 2. Search principle: if both alternatives are recognized, it is assumed that the decision-maker has access to a set of cues/attributes of the options → a search is conducted on these cues in order of relevance/validity until a discriminating cue is found Validity is defined in terms of the probability that the cue will actually help in discrimination TAKE-THE-BEST Stopping Rule: The first cue that allows us to discriminate stops the process TTB is a form of lexicographic strategy → a strategy in which cues are evaluated in a predetermined order (EX. alphabetical order) TTB is lexicographic because if the initial cue does not help a person to discriminate, they evaluate a series of cues ordered by their validity to discriminate, proceeding sequentially until discrimination is achieved THE ROLE OF FEEDBACK Feedback seems to play a role if the environment is relatively simple because feedback usually does not provide clear indications about cue/criterion relationships When cues and the criterion are expressed in quantitative terms relative to the same variable, or if the task structure is clear/simple, feedback is very helpful  PROBABILITY 101 Probabilistic Judgment ≠ Probability theory BASIC PRINCIPLES Events are represented as subjects of a Sample Space (a larger set); an additive measure is proposed to assign probabilities to events Assignment of numbers between 0-1 to events representing their degree of uncertainty Probability of an Event: The number of favorable outcome divided by the total number of outcomes The Additive Rule for Unions: For any two events (A & B), the probability of their union (P(AUB) = When two events A & B are mutually exclusive: P(A∩B) = 0 and P(AUB) = P(A) + P(B) The rule for calculating P(A∩B) depends on the idea of Independent & Dependent Events - Independent Events: Two events, A & B are independent if the occurrence or nonoccurrence of one of the events does not change the probability of the occurrence of the other events - Dependent Events: Conditional Probability: The probability that A occurs GIVEN THAT event B has occurred BAYES RULE BAYES: MORE HYPOTHESES Let S1, S2, S3…Sn be mutually exclusive events with prior possibilities of P(S1), P(S2)... P(Sn) If event A occurs, then the posterior probability of Si, given that A has occurred is = LAW OF LARGE NUMBERS: If E is an event & Pl is its probability of success, then the relative frequency of success in n independent trials converges towards p as n approach (positive) infinity CONJUNCTION RULE: In Probability Theory, events are represented as subsets of a larger set called the Sample Space and an additive measure is proposed to assign probabilities to events  PROBABILISTIC REASONING WHAT IS THINKING? Thinking: An umbrella terms to indicate a set of distinct high-level cognitive processes (reasoning, categorization, decision-making); also considered a generic synonym for information processing - Ponser (1973): Obtaining a new representation through mental operations; as part of cognitive processes in problem-solving & learning; refers to how individuals can manipulate existing mental images or concepts to form new understandings or representations of a problem or situation - Hastie & Dawes (2001): Creation of mental representations that are not in our immediate environment → thinking as an extension of perception, an extension that allows for people to fill in the gaps of the representations produced by their perceptual processes & infer causal relationships: - EX. Seeing a green wall is not thinking, imagining how it would look if painted blue is thinking - EX. Noticing a patient has yellow skin is not thinking, hypothesizing that they have a liver problem is thinking The mind engages in thinking when new information becomes available that doesn't have an immediate connection to current sensory or perceptual inputs → this type of cognitive processing occurs when the brain encounters data or concepts that are not directly observable in the immediate environment Importance of action: the “something extra” beyond perception guides the organism’s action - Aristotle: Two modes of knowledge of the world: Sensory experience VS Thinking (what is not directly perceivable) - Smith (1995): Mental representation of certain aspects of the world & the manipulation of these representations of beliefs to obtain new beliefs, which can be used to pursue some goal; we mentally represent aspects of the world & then manipulate those representations to form new beliefs that help us achieve specific goals - Johnson-Laird (1992): What lies between perception & action; the crucial role of mental representation & reasoning Mental Models: Humans act on the basis of their mental models, not on reality; very often, humans deceive themselves into believing that their mental models are faithful representations of reality Deductive Reasoning: Logical process where conclusions are drawn from general principles or premises to reach a specific, logically certain outcome → follows a top-down approach; starts with broad, established truths or rules & applies them to a particular case to deduce a conclusion Inductive Reasoning: Logical process in which general principles or conclusions are derived from specific observations or examples → follows a bottom-up approach; starts from individual instances or facts & uses them to form broader generalizations or theories Inference: The process, deductive or inductive, through which one moves from a proposition assumed to be true to a second proposition whose truth is derived from the content of the first by means of appropriate rules - Deductive Inferences: If the initial statements are true, then the conclusion must also be true; it is logically impossible for the premises to be true & the conclusion to false at the sametime - Inductive Inferences: The truth of the premises does not guarantee the truth of the conclusion; even if all premises are true, the conclusion may still be false - EX. Probabilistic reasoning THE RATIONALITY DEBATE Are humans rational? To predict/evaluate behavior, there needs to be a “model”; an abstract representation of a phenomenon that captures only the relevant aspects for making optimal predictions Rationality (according to some fields): A person’s beliefs & preferences are internally consistent - In economics, a person is considered rational as long as their beliefs & preferences are internally consist; they are considered an Econ - EX. rational person can believe in ghosts if all their beliefs are compatible with the belief in ghosts - Humans are not rational according to this criterion; people’s beliefs & preferences frequently lack internal consistency, are affected by biases, emotions etc. Normative Criteria: The criteria for rationality; can often be met (assuming that an individual knows them) by applying specific algorithms to solve a certain problem PROBABILISTIC REASONING & CHOICE People constantly have to make judgements about the probability of uncertain events; however, people usually don't know Probability Theory In many situations, its either not possible to apply it or the computational resources required are excessive Basic Theoretical Positions: - Rational Choice Theory: A framework used to understand how individuals make decisions; assumes that people are rational agents who seek to maximize their utility when faced with decision-making problems under conditions of uncertainty →rational agents evaluate potential outcomes of different options & choose the one they believe will result in the best possible outcome - Algorithm: Sequence of procedures that guarantees the solution to a problem if applied in the correct way; very demanding in terms of resources  - Deliberate Mind: Capable of applying abstract rules to assess whether reasoning is logical → requires effort & knowledge of the rules - ALGORITHM - Bounded Rationality: Argues how the classical economic definition of rationality is psychologically unrealistic; humans face limitations in their cognitive abilities (memory, attention etc) → optimal answers are not sought but rather satisfactory ones; Heuristics are commonly employed - Heuristics: Fast form of reasoning - Does not require knowledge of normative principles related to the problem’s domain - Does not need the “calculation” normally required when applying an algorithm to data - Does not guarantee reaching the correct solution & is subject to biases Associative Mind: Characterized by one’s beliefs, intuitions & mental shortcuts - HEURISTIC THE HEURISTICS & BIAS RESEARCH PROGRAMME Heuristics allow us to abstain sufficiently good solutions (even if not optimal) Depending on the situations, they can lead to biases/systematic distortions in reasoning AVAILABILITY HEURISTIC Availability Heuristic: Uses the ease of recall as a criterion for determining probability; Since applying an algorithm is demanding, people use the Availability Heuristic REPRESENTATIVENESS HEURISTICS: CONJUNCTION FALLACY Representativeness Heuristics: Uses a similarity criterion for determining probability; estimating the probability of an event based on the degree to which it is similar to the essential characteristics of the population from which it was drawn, or whether it reflects the salient characteristics of the process that generated it → LINDA PROBLEM Use of the Representativeness Heuristics = committing the Conjunction Fallacy bias Conjunction Fallacy: Cognitive error where people mistakenly believe that the combination of two events is more likely than a single event on its own (EX. Linda problem) REPRESENTATIVENESS: DISJUNCTION FALLACY Bar-Hillel & Neter (1993): People consistently ranked the SUBordinate category as MORE probable than the superordinate → violates the law of probability - A subordinate category cannot be more probable than a superordinate category that contains it → literature is a sub category than humanities, but people said that humanities would be less likely than literature; in this case people thought literature would be more representative than humanities = representative heuristic Disjunction Fallacy: Cognitive error where people underestimate the probability of a disjunction of events; they perceive the likelihood of one event or another occurring as less likely than the individual probability - In reality, the probability of at least one event happening is always equal to or greater than the probability of either one event happening on its own (P(AUB) > P(A)) REPRESENTATIVENESS: BASE-RATE FALLACY Base Rate: The general or prior probability of an event or characteristic occurring in a population, without considering specific additional information Using the Representative Heuristic, we are induced to say male (being a soccer fan “represents” the male stereotype) but we have seen that from an algorithmic/normative POV (based on the probability theory & using Bayes Theorem), this is not true Base-Rate Fallacy: Ignoring the base rate Why people neglect Base Rate information: - People are faced with a difficult probability problem (complex Bayesian computation) - There is a closely related (however, incorrect) answer that is readily accessible, so they give this answer (shortcut/heuristic-based; more readily-accessible in the mind/intuitive answers come to mind more quickly & feel more natural, even if incorrect) - Robust results: It is well-documented & there are consistent findings of this happening → people systematically neglect base rates in favor of more intuitive, heuristic-driven answers THE IMPORTANCE OF FREQUENCY FORMAT 1994/1995: Gigerenzer challenged the conclusion that people fail to reason in accordance with the laws of probability (Tversky & Kahneman) → argued that the errors in probabilistic reasoning happen NOT because people are inherently bad at probability, but because the problems are often framed in terms of probabilities rather than natural frequencies (a format that humans are more naturally equipped to handle) The errors people make in probabilistic reasoning often stem from how problems are framed; when recast in terms of natural frequencies, people make fewer mistakes → suggests that the human mind is more adept at processing probabilities in a more intuitive format Factors that drive this facilitation of reasoning: 1. Single-case probabilities are ambiguous; often abstract & difficult for people to interpret 2. Human cognitive system evolved in environments where uncertain information was experienced in terms of natural frequencies; the primitive man, was not evaluating the probabilistic world in terms of probability; from an evolutionary POV, it would be more natural to think based off of natural frequencies; this could be how ancient humans conceived probability Natural Frequencies: Refers to actual, real-world situations with frequencies updated in a sequential fashion; grounded in actual observations that occur over time; allow people to track how often certain events happen in a way that feels more intuitive & concrete - Reduces abstraction - Fits within human cognition → humans evolved to deal with information that accumulates over time, through direct experience in the environment IMPORTANCE ON CAUSAL MODELS “If my underwear is blue, then my socks are green My socks are green, therefore my underwear is blue” → Affirming the consequent; people usually commit this mistake “If I fall into the sewer, then I will have to take a shower I took a shower, therefore I feel into the sewer” → Although this also exemplified affirming the consequent, people tend to make fewer errors with these kinds of cause-effect relationships Due to: - Causal reasoning: Humans are more familiar with multiple possible causes, this wider understanding of causes helps people recognize the fallacy more easily (EX. there are many reasons to take a shower) - Familiarity with multiple outcomes: Humans know that there can be many causes to an event, so a fallacy becomes more obvious since people can easily identify alternative reasons for the cause of an event (EX. there are many reasons to take a shower, not just because you fell into a sewer) REPRESENTATIVENESS: GAMBLER’S FALLACY Gambler’s Fallacy: Cognitive bias when someone believes that the outcome of a random event is influenced by previous outcomes, despite each event being independent - The Associative Mind is usually characterized by beliefs & intuitions that suggest that a random process must produce irregular outcomes; there must not be too long of sequences of the same outcome - Committing this Fallacy often depends on the context The Sequence where the outcome is R is judged “more probable” because it is more similar to the salient characteristics of the random process that generated it (more representative) Law of Large Numbers: If event E has P probability of success, then the relative frequency of success in n independent trials converges towards P as n approaches (positive) infinity - Gambler’s Fallacy is associated with a misunderstanding of the Law of Large Numbers - At infinity, a random process presents equiprobability AND irregularities; human intuition says that a sequence of outcomes produced by a random process must show both equiprobability & irregularities; they believe that the Law of Large Numbers should apply to small samples as well RANDOMNESS PERCEPTION Events observed can arise from - Fortuitous Coincidences: Random processes - Effective Regularities: Systematic or regular processes Understanding how to differentiate between these two is crucial for accurate reasoning & decision-making SUPPORT THEORY (TVERSKY & KOEHLER, 1994) Standard probability theories assign probabilities to events; Support Theory assigns probabilities to descriptions of events - People’s intuitive judgements are sensitive to the way in which events are described (EX. alternative description of the same event can lead to different estimate) - People estimate probabilities on the basis of representations of events rather than on the events themselves - Subjective probabilities are derived from judgements of the strength of evidence/support in favor of the hypothesis Subadditivity Pattern: The probability assigned to a hypothesis will typical increase if it is unpacked into disjunction of components; the tendency to assign lower probabilities to the joint occurrence of two + events than the sum of the probabilities assigned to each event separately → arises in cases of uncertainty where the perceived likelihood of combined events appears less than their individual components - Implicit Subadditivity Pattern: Support of hypothesis A ≤ the support for a disjunction formed by unpacking A into exclusive components → tendency to intuitively underestimate the probability of combined events 𝑠(𝐴) ≤ 𝑠(𝐴1 𝑜𝑟 𝐴2) - EX. Comparison between dying from homicide VS dying from homicide by an acquaintance or stranger - Explicit Subadditivity Pattern: Support of hypothesis A ≤ the sum of the supports for each of the subcomponents → consciously & intentionally assign a lower probability to the occurrence of combined events than would be predicted by the sum of their individual probabilities; 𝑠(𝐴) ≤ 𝑠(𝐴1) + 𝑠(𝐴2) - EX. Comparison between dying from homicide VS the sum of the separate supports given to dying from homicide by an acquaintance + dyinging from homicide by a stranger  RATIONALITY INTRODUCTION Rationality may be defined as the quality of being guided by or based on reason → in this regard people act rationally if they have a good reason for what they do, or belief is rational if it is based on strong evidence Classic positions: - Reason-responsiveness accounts - Coherence-based accounts - Goal-based accounts It's not easy to define rationality so we have to focus on specific fields of research with different definitions among them REASON - RESPONSIVENESS ACCOUNTS Rationality is defined in terms of reasons - EX. Dark clouds are a reason for taking an umbrella, which is why it is rational for an agent to do so in response Crucial point → it is not sufficient to merely act accidentally in accordance with reason; responding to reasons implies that one acts intentionally because of these reasons - EX. The agent eats a fish contaminated with salmonella, but since the agent could not have known this fact, eating the fish is rational for them - Because of such problems, many theorists have opted for an internist version of this account → means that the agent does not need to respond to reasons in general, but only to reasons they have or posses; their is an intentionality aspect “Rationality consists in responding correctly to beliefs about reasons” → it is rational to bring an umbrella if the agent has strong evidence that it is going to rain AND it would be rational to leave the umbrella at home if it is going to rain BUT the agent doesn't know - Rationality no longer requires the agent to respond to external factors of which they could not have been aware - Issues with this account: - 1. There are usually many relevant reasons to do or not do something & some of them may conflict with each other (EX. While salmonella contamination is a reason against eating fish, it's good taste & desire to not offend the host are reasons in favor of eating it → this problem is usually approached by weighing all the different reasons, this way one does not respond directly to each individual reason but instead to their weighted su) - 2. Cases where reasons require the agent to be irrational, leading to a rational dilemma (EX. If terrorists threaten to blow up a city UNLESS the agents form an irrational belief, this a very weighty reason to do all in one’s power to violate norms of rationality) COHERENCE - BASED ACCOUNTS A person is rational to the extent that their mental states and actions are coherent with each other (internal coherence among the agent’s mental states) Diverse versions of this approach exist that differ in HOW they understand coherence & what rules of coherence they propose → two types of coherence: - Negative Coherence: Requires the ABSENCE of contradictions (uncontroversial) - Positive Coherence: Supports that different mental states PROVIDE for each other (typically including explanatory & casual connections); not only a matter of absence of contradiction, but all of the mental states must point to the same direction Issues: Rational dilemma when mental states clash with each other; in this case it is impossible to be rational Possible Solution: Rationality requires not to obey ALL norms of coherence, but to obey as many norms AS POSSIBLE; if your contradictory belief is only a small part of all your beliefs as a whole, its okay GOAL - BASED ACCOUNTS Characterizes rationality to the goals it aims to achieve Theoretical (epistemic) VS practical (instrumental) rationality - Theoretical (epistemic) rationality: Aims at epistemic goals; acquiring truth & avoiding falsehood - Practical (instrumental) rationality: Aims at non-epistemic goals; moral, prudential, political, economic etc goals; usually understood in the sense that rationality follows these goals but does not set them Four conceptions based on the goals it tries to achieve (Frankena): - Egoist: Rationality implies looking out for one’s own happiness - Utilitarian: Rationality entails trying to contribute to everyone’s well-being or to the greatest general good - Perfectionism: A certain ideal of perfection, either moral or non-moral, is the goal of rationality - Intuitionist perspective: Something is rational if & only if it conforms to self-evident truths, intuited by reason Issue: Ignores the role of the evidence or information possessed by the agent INTERNALISTS VS EXTERNALISTS Both sides agree that rationality demands & depends in some sense on reasons They disagree on what reasons are relevant or how to conceive those reasons → Internalists focus on mental state, Externalists on the environment Internalists: Understand reasons as mental states; perceptions, beliefs, desires Externalists: See reasons as external factors about what is good or right; they state whether an action is rational also depends on its actual consequences THREE APPROACHES Normative: Focus on principles underline a rational choice & defining criteria to evaluate a “rational” behavior; seen before through probabilistic reasoning; people’s choices compared to a normative criterion (probability theory) Descriptive: Focus on studying the actual behavior of people & developing psychological models of individuals Perspective: Focus on developing interventions/technologies in order to do better decisions 17th century- 1850: A lot of the earlier approaches thought that they were defining laws of thought; when probability theory was being developed, it was thought as psychology/the laws of thought Homo Economicus: This kind of idea is still around today; the idea that within economics, the classical agent is a 100% rational human - There are a lot of theories in economics that just assume that agents are rational; why do they assume this? → the point is that if someone is behaving in an irrational way, you just don't care about this behavior because it outside of the market 1995; Piaget & Inhelder: Psychologist who studied child development; a child year by year acquires more logical laws, they become more rational over time; his idea just assumes the idea that the human mind has inside the normative principles, it's just a matter of time before it comes out 1955/1982; Simon: Started the bounded rationality approach; people behave in a rational way but the human mind has limitations/its bounded 1970s; Kahneman & Tversky: Focused on people’s mistakes; heuristics lead people to make mistakes 1990s; Gigerenzer: Bounded Rationality (Simon) ideas evolved into those of Gigerenzer; Simon & Gigerenzer say that people try to approximate rational choices by using simple & effective strategies 2000s; Griffiths & Tenenbaum: Another perspective of rationality ARE THE LAWS OF LOGIC/PROBABILITY THEORY THE LAWS OF THOUGHT? In philosophical tradition, the laws of logic coincide with those of thought Formal Rule Theory/Theory of Mental Logic: There are “logical laws” inside the human mind based on an internal system of formal rules (even if principle & inferences are not necessarily standard ones) - Errors, hesitations, longer response times etc. are generally explained in terms of incorrect constructions of premises or processing problems (limits of working memory); People are not going to represent the problem in the correct way because they didn't understand it correctly, but if they were to understand it correctly, then they would be able to make rational decisions since humans are inherently rational when put in the right situation because the human mind is naturally logical - Several problems to this theory & there is contrary evidence WASON SELECTION TASK Task is designed to show how people often struggle to apply formal logic in everyday decision-making and how certain cognitive biases influence reasoning → DISPROVES the philosophical tradition Demonstrates that humans struggle with abstract logical reasoning, particularly when it involves falsifying a hypothesis - Variations of the task have shown that people are more successful when the task is framed in a familiar real-world context (EX. legal drinking age example) Why is this mistake made? When reasoning abstractly, people fail because: - The task doesn't align with natural schemas. - There’s no intuitive or survival-based relevance for formal logic puzzles. Pragmatic Schema Approach: Our beliefs, experience, mental scripts all guide (where possible) our reasoning This perspective argues that humans don't naturally reason using abstract logic but rely on pragmatic schemas developed from experience. These schemas are: - Domain-specific: Activated in familiar, real-world contexts. - Governed by rules about permissions or obligations (deontic reasoning), such as legal or social norms. - Deontic (Real-World) Context: Changing the task to something practical, like enforcing a rule about drinking age, activates domain-specific schemas. People unconsciously use these familiar rules to make correct selections. Rational Analysis (Bayesian Approach): Focus on the Marr computational level; The Bayesian view frames reasoning as an adaptive process where the brain solves tasks that have been evolutionarily relevant: - Survival Relevance: The human mind evolved to handle uncertain, probabilistic reasoning in practical environments, not to solve formal logic puzzles. - Task Definition: When framed as finding evidence or testing a probabilistic hypothesis (e.g., likelihood of rule violation), humans reason adaptively. Bayesian principles suggest people unconsciously weigh the relevance of evidence rather than rigidly applying logical rules. - Bayesian Lens: When tasks are reframed as probabilistic predictions or involve relevance to everyday scenarios, the brain "adapts" by applying intuitive reasoning. For example, detecting rule violations is analogous to identifying risks, a vital evolutionary skill. A COMPLEX DEBATE Pragmatic scheme (domain-specific rules) cannot explain ALL facilitation effects; there are linguistic-contextual effects that affect the answers There are experiments with scenarios that refer to non-deontic rules that are easily solved → due to contextual factors & linguistic factors RESEARCH IN ECONOMICS Homo Economicus: Hypothetical agent who is 100% rational - has complete information about options available for choice - perfect foresight of the consequences from choosing options available - ability to solve optimization problems - identifies an option which maximizes the agent’s utility Evolutions of the Economic Man (Econs): - 1844: Mill’s description of a hypothetical, self-interested individual who seeks to maximize his personal utility - 1871: Jenon’s mathematization of marginal utility to model an economic consumer - 1921: Knight’s portrayal of the slot-machine man of neo-classical economics with perfect foresight - 1940s: Changed the focus of economic modeling from REASONING behavior to CHOICE behavior This idea in economics of a man who wants to “maximize their own interest” has over time become very important in economics → behavioral economics challenges that RESEARCH IN ECONOMICS & PSYCHOLOGY: BOUNDED RATIONALITY Bounded Rationality (Simon): An alternative basis for mathematical & neoclassical economic modeling of decision-making Simon: Thought the shift in focus from reasoning behavior to choice behavior was a MISTAKE → mathematical & classic economic models assuming that the agent is rational is not in line with the real world; actual people are not rational like Homo Economicus - Proposed to focus on the costs in effort of using a particular decision-making procedure with the cognitive resources available - Emphasized that the decision-making process is not free of cost; each cognitive task requires mental effort & time - The effectiveness of decision-making models could be evaluated by comparing performance (accuracy) within bounded resources - Instead of aiming for perfection, decision-makers are better off using SATISFICING strategies →yielding satisfactory rather than optimal outcomes Procedural/Bounded Rationality: Effectively managing the trade-off between the costs & quality of a decision Simon Key Contributions: Accuracy-effort trade-off & Satisficing Strategy Simon & Good critiqued the cognitive demand of Subjective Expected Utility Theory → proposes that rational decisions involved CALCULATING probabilities and utilities for each possible outcome; unrealistic for real-world because of high computational & cognitive costs - Simon & Good emphasize that decision models must consider the PRACTICAL CONSTRAINTS on human cognition - Rationality is not about perfection but about maximizing outcomes within the bounds of available resources; simplified decision-making strategies can be understood as rational & efficient Satisficing: The strategy of considering the options available until you find one that meets or exceeds a predefined threshold for a minimally acceptable outcome; satisfactions + suffice Bounded rationality must account for: - Behavioral Constraints: Bounds on computation/limitations within an individual’s cognitive capacities → humans cant process unlimited information or compute complex probabilities for every choice - Environmental/Ecological Structure: A structured context that shapes the effectiveness of different decision strategies; depending on the state of the environment a decision-maker is in, how well or not-well certain decision-making strategies may work THE IMPORTANCE OF ECOLOGICAL RATIONALITY: EGON BRUNSWIK Egon Brunswik: Among the first to apply probability/statistics to the study of human perception; emphasizing the role ecology plays in the generalizability of psychological findings Brunswik’s Lens Model: Formulated around his ideas about how behavioral & environmental conditions are used; emphasizes that both one’s behaviors & the surrounding environment influence how they interpret nearby “cues” to make inferences about unseen aspects of the natural or social world (EX. dark clouds (cue) means rain storm is near (unseen aspect of environment) Ecological Validity of Proximal Cues: refers to a given cue’s capacity for advancing an individual with useful information about some distant object within an environment Assessments of performance for an individual amount to the comparison between the weight they ascribed a given cue, to the cue’s actual weight in a given situation - Brunswik’s Lens Model reflects both the “classical rational” POV while putting an emphasis on the environment FROM SIMON TO KAHNEMAN & TVERSKY Kahneman & Tversky (1982): Heuristics & bias research programme - Human reasoning on its own often falls into predictable mistakes & biases; to make better choices, it's important to understand these biases & find ways to avoid them - A frequent use of heuristics sometimes leads humans to commit systematic mistakes (biases) - Empirical evidence AGAINST axioms of economic theory → Prospect Theory ECOLOGICAL RATIONALITY: FROM KAHNEMAN & TVERSKY TO GIGERENZER Ecological Rationality: Claims that the rationality of a decision depends on the circumstances in which it takes place, aiming to achieve the best outcome for that particular setting - What is considered rational under the rational choice account (EX. subjective expected utility) might not always be considered rational under the Ecological Rationality account Rational Choice Theory focuses on making sure choices are logically consistent, while Ecological Rationality emphasizes making decisions that work well in real-world situations - Gingerenzer argues that some observed behavior violates Rational Choice Theory BUT is rational in some environments → the rationality of an action not only depends on internal criteria but also on the structure of the environment Ecological Rationality defines, in mathematical detail, when & why a person should choose certain decision-making methods based on specific environmental conditions to make more accurate & effective decisions FAST & FRUGAL HEURISTICS - GIGERENZER According to Gigerenzer → the biases & heuristics program mistakenly classifies all biases as errors despite evidence pointing to some biases in human psychology being adaptive In contrast, Kahneman & Tversky → Maintain that the dispute is merely terminological Gigerenzer’s Adaptive Toolbox: Consists of simple heuristics that help people solve various problems - These heuristics leverage the benefits of our mind’s cognitive limitations; in certain contexts it could provide advantages in terms of frugality & speed of decision-making RATIONAL ANALYSIS We can better understand how the human mind works by recognizing that it has evolved specifically to adapt to its environment/interacting in ways that fits its surrounding; suggests that our mental processes are shaped to function well within the world we live in → Ecological Rationality Rational Analysis: A form of investigation of psychological processes that considered the constraints deriving from how our mental processes are designed to help us adapt effectively to our environments; understanding the logic behind behaviors and decisions, especially in the context of environmental constraint & adaptive strategies Rational Analysis Steps: - Define the goal or purpose behind the behavior: Focus is on WHY behavior reflects an attempt to achieve some goal efficiently given the individual’s environment → assumes that a behavior reflects a RATIONAL response to achieve a goal efficiently given the individual’s environment - Identify constraints: Constraints in the real-world & how they influence behavior (EX. limited time, cognitive capacity, incomplete information etc) - Analyse how decisions are made to achieve goals within constraints: Looks at the princess & mechanism people use to navigate constraints & achieve their objectives → may include using heuristics, adaptive shortcuts or probabilistic reasoning - Evaluate the alignment between behavior and optimal outcomes: determines whether the observed behavior effectively achieves the goal under the given constraints; even if the behavior seems irrational a first it might be optimal within the specific context SOME OBSERVATIONS ABOUT RATIONAL ANALYSIS Rational Analysis is NOT a theory of psychological processes; it does not specify the representations or algorithms with which we arrive to a solution Be careful in distinguishing the MEANING of the tasks proposed in the CONTEXT of Rational Analysis and the MEASURE of performance on logical or probabilistic problems Even if we are intuitively able to correctly answer the task described, very few humans are able to carry out Bayesian calculations There is empirical evidence that can be explained in terms of Rational Analysis only by making very strong assumptions (EX. Conjunction Fallacy) THE CONJUNCTION FALLACY EX. The Linda Problem CONJUNCTION FALLACY: RATIONAL ANALYSIS The idea of Rational Analysis is to determine which function/behavior/decision the cognitive system optimizes → argues that human cognitive processes have evolved to perform well in the environments in which they typical operate in - Instead of labeling certain decisions/biases as irrational, Rational Analysis seeks to understand WHY the mind might have developed these patterns & what their benefits might be Relevance for adaptation to the environment (Ecological Rationality): Rational Analysis views cognitive processes as adaptations, optimized to help us make decisions that are effective in specific ENVIRONMENTAL contexts In this context, fallacy is NOT an error; according to Rational Analysis, applying context-specific heuristics that conflict with strict probability rules isn't necessarily an error but rather reflects a decision-making process that prioritizes relevance over strict logic THE “QUANTUM COGNITION” APPROACH Busemeyer & Bruza (2013): Proposed the use of the probability theory employed in quantum physics instead of classic probability theory - Quantum Probability Theory is based on vectors & geometric projections - The definitions of Event is different compared to Classical Probability Theory: - Classical Theory: Subset of a set - Quantum Theory: Subspaces of a vector space - Instead of using the P function to assign probabilities to events, Quantum Probability uses a state vector, S to assign probabilities Comparison with a state vector labeled as S and event A: Classical: - Each unique outcome is a member of a set of points called the Sample Space - Each event is a subset of the sample space - State is a probability function, P, defined on subsets of the sample space Quantum: - Each unique outcome is an orthonormal vector from a set that spans a vector space  - Each event is a subspace of the vector space - State is a unit length vector, S WHY QUANTUM? Principles from Quantum Theory resonate with psychological institutions & conceptions about human cognition & decision - Ambiguity, uncertainty - Constructive view of judgment SUPER POSITION: AMBIGUITY, UNCERTAINTY Superposition → a quantum system can be in multiple states at the same until it is measured CONSTRUCTIVE VIEW OF JUDGEMENT CAVEAT NOT a physical/neurobiological theory of brain/NOT a theory of consciousness → It is a mathematical/computational theory about human cognitive and decision behaviors - Uses a quantum formalism to build cognitive and decision models (new modelling toolboxes, new theoretical perspectives and coherent framework) SLOMAN (2014) How things are framed affects how they are valued; our brains often require complex representations to make sense of information; psychology is full of order-effect (EX. Gore-Clinton Example) Different domains require different normative analyses; arguing whether Quantum Probability or Classical Probability is the right normative theory depends on prevailing conditions  DECISION-MAKING INTRODUCTION EX. You have to choose between: - (A) 100 euros for sure (B) lottery ticket with 50% chance of winning 200 euros or nothing Almost ALL people people choose option (A); preference the CERTAINTY/show RISK AVERSION Is it possible to mathematically quantify the value of each option to establish the most convenient one? EXPECTED VALUE (PASCAL) Pascal proposed the use of Expected Value (EV); a fundamental for rational choice theory Expected Value (discrete variable): the sum of the products of each value and the respective probability ↑Both choices have the SAME expected value, the prediction would be “indifference” → however, empirically, almost ALL people chose option (A) PASCAL (1623 - 1662) Expected Value identifies critical variable: probability and gains/loses - Probability of an outcome - The gain or loss associated with that outcome By weighting these together, humans can make choices that maximize benefits Pascal identified an algorithm for making decisions→ EX. expected value of deciding to believe in God or not Pascal’s Expected Value is a normative, non-descriptive theory - Normative because its prescribes HOW people SHOULD make rational decisions - Non-descriptive because it serves as GUIDE for rational choice, but does not account for real-world decision-making factors (EX. emotional influence, cognitive biases etc.) From a normative POV there are problems: - St. Petersburg Paradox ST. PETERSBURG PARADOX St. Petersburg Paradox: A paradox involving the game of flipping a coin where the expected payoff of the lottery game is INFINITE but nevertheless SEEMS to be worth only a very small amount to participants - A situation where a naïve decision criterion that takes ONLY the Expected Value into account predicts a course of action that presumably NO actual person would be willing to take When making decisions that involved some UNCERTAINTY, people did not always try to MAXIMIZE MONETARY GAIN, but rather tried to MAXIMIZE UTILITY → a term encompassing their personal satisfaction & benefit There is a direct relationship between money gained & utility, but it DIMINISHES as the money gained INCREASES → “the determination of the value of an item must not be based on the price, but rather on the utility it yields” Log utility model (Bernoulli) suggests that the satisfaction/utility a person gains from money does not increase in DIRECT proportion to the amount of money they have → increases more SLOWLY as wealth GROWS → diminishing marginal utility; 𝑈(𝑤) = 𝑙𝑛(𝑤) Weber-Frechner law supports this idea concerning stimulus intensity→ as a stimulus increases, a person’s perception of that increase DOESN'T grow linearly but in a LOGARITHMIC curve; also applies to the principle of wealth NEOCLASSICAL ECONOMICS/RATIONAL CHOICE THEORY Neoclassical economists believe that a consumer’s first concern is to maximize personal satisfaction → utility Consumers make purchasing decisions based on their evaluations of the utility of a product or service Coincides with rational behavior theory → states that people act rationally when making economic decisions - People are rational in making choices between identifiable and value-associated outcomes & an individual’s purpose is to maximize utility LOTTERIES: RISK VS UNCERTAINTY SITUATIONS Within economics, there is a distinction between:  - Situation of risk: the decision-maker has taken some action that leads to an objective probability distribution over a set of possible outcomes - Situation of uncertainty: no objective probabilities that are universally agreed upon can be identified EXPECTED UTILITY THEORY axioms of rationality are the basic principles that guide rational decision-making 1. Completeness: - A rational decision-maker can compare any two options & have a clear preference over one or the other or see them as equally good - They don't leave any options undecided or “incomparable” 2. Transitivity: - If a person prefers A over B & B over C, then they should prefer A over C → choices are consistent & avoid contradictory preferences 3. Independence: - If A and B are present with a third irrelevant caption, C, the choice between A and B shouldnt change just because C is added - A rational person’s preference between two options should remain stable 4. Continuity: - If someone prefers A to B and B to C, a mix of A and C should be valued as equal to B Under the axioms of rational behavior, a decision-maker faced with risk/probabilistic outcomes of different choices will behave as if they are maximizing the Expected Value of some function defined over the potential outcomes at some specified point in the future → von Neumann-Morgenstern utility function Any individual’s preferences can be represented on an interval scale & the individual will ALWAYS prefer actions that MAXIMIZE expected utility SUBJECTIVE EXPECTED UTILITY (SEU) Subjective expected utility characterizes the behavior of decision-makers in the situation of uncertainty The theory of subjective expected utility combines two subjective concepts: - A personal utility function - Personal probability distribution CONSIDERATIONS ABOUT THE AXIOM OF RATIONALITY A violation of rationality is not a form of irrationality or madness The axioms are no more than ASSUMPTIONS about human behavior & their reasonableness must be tested by empirical evidence A large number of tests have REJECTED the axioms of rationality → the challenge of behavioral economics is to produce a behavioral decision theory that is: - In conformity with evidence - Sufficiently tractable Invariance: different representations of the same choice problem should NOT yield different preferences → asian disease problem PROSPECT THEORY Psychological theory that describes how people make decisions involving risk & uncertainty Unlike traditional economic theories which assume that people are perfectly rational, prospect theory explains why people often make seemingly irrational choices → particularly when it comes to gains & losses Describes how individuals assess their loss and gain perspectives in an asymmetric manner (loss aversion) Value Function: People think in terms of changes from their current situation rather than final wreath levels The value function is asymmetric→ reflects the idea of loss aversion (people strongly prefer avoiding losses over acquiring equivalent gains) v is the value function that passess through the reference point → s-shaped and asymmetrical PROBABILITY WEIGHTING FUNCTION In Prospect Theory, people don't perceive probabilities linearly → people tend to overweight small probabilities and underweight large probabilities ↑𝝅 is a probability weighting function that captures the idea that people tend to overreact to small probability events, but underreact to large probabilities  ↑For low objective probability, people tender to overestimate → believing unlikely events are more likely than they actually are Very sensitive to the difference between impossible VS possible; explains the rationale behind insurance, lotteries etc. ↑the curve intersects the identity line around objective probabilities of 20% → people interpret/perceive probabilities around 20% correctly The majority of the central portion of the curve is essentially flare → between 20 - 80% probability, people perceive very little difference in relevance ↑The final part of the curve shows the underweighting of large probabilities → a large difference in objective probabilities results in a small difference in subjective probabilities The value function is defined on changes IN wealth rather on FINAL wealth levels Gains & losses are defined RELATIVE to a reference point - Perceptions of both gains & losses are characterized by diminishing marginal sensitivity in either direction THE FOURFOLD PATTERN OF RISK ATTITUDES According to the Prospect Theory, people are risk-averse in the domain of gains and risk-seeking in the domain of losses This is because the focus is on non-small probabilities → when considering the entire range of probabilities, a fourfold pattern of risk attitudes emerges - Risk-Averse in Gains: When people stand to gain, they prefer certain, smaller rewards over larger, uncertain ones. For example, most would choose a guaranteed $100 over a 50% chance to win $200. - Risk-Seeking in Losses: When facing potential losses, people often take risks to avoid losing. For example, they might choose a 50% chance of losing nothing over a guaranteed loss of $100. PT: LOSSES LOOM LARGER THAN GAINS Asymmetry of people’s reaction to pain versus pleasure is hypothetically rooted in an evolutionary principle → the environment punishes those who ignore danger signs more than it rewards those who pursue signs of pleasure Two well known phenomena: - Endowment effect (Thaler, 1980): Once a person acquires something, they are often reluctant to give it up, even if offered a price that is more than the person himself would have paid for the object - Status quo bias: related to the endowment effect → preference to remain in the same state (the status quo) rather than take a risk & move to another state (EX. Love It or List It TV show) → explained by the potential loss incurred by the shifting from the status quo looming larger than the potential gains LOSS AVERSION AND RISK AVERSION Loss aversion: people value losses more than gains Certainty effect: people prefer certain outcomes and underweight outcomes that are only PROBABLE - Avoiding risk when there is a prospect of a sure gain - Seeking risk when one of their options is a sure loss Risk aversion: People choose an outcome thats certain over one that's uncertain Novemsky & Kahneman (2005) extend Thaler’s (1980) idea of using loss aversion (fear of disappointment/fear of a large loss) as an explanation of exchange disparities to risky choice - Their most important conclusion → risk aversion is not a separate tendency from loss aversion when subjects are presented with equal probabilities of all outcomes/that have balanced risks - The idea that risk aversion could, at least partly, be caused by loss aversion is well recognized Loss aversion is the main driver behind what we perceive as risk-averse behavior in many cases. RICHARD THALER CONTRIBUTION Endowment effect Mental accounting → sunk cost fallacy Nudge theory ENDOWMENT EFFECT Endowment effect (Thaler, 1980): Once a person acquires something, they are often reluctant to give it up, even if offered a price that is more than the person himself would have paid for the object The amount of money that owners of an object are willing to accept in exchange for the object is the willingness to accept (WTA) The willingness to pay (WTP) for the object is the amount of money that individuals are willing to pay to buy an extra unit of the object Thaler noted the presence of exchange asymmetries → WTP < WTA increasing over time as the duration of ownership increases → explained by loss aversion Endowment effect applies to goods but not to money or to goods purchased for resale; loss aversion does not apply to the buyer’s act of giving up cash ENDOWMENT EFFECT: KAHNEMAN, KNETSCH & THALER (1990) Valuation paradigm: people’s maximum WTP to acquire an object is typically LOWER than the least amount they are WTA to give up the same object when they own it - EX. Cornell bookstore coffee mug experiment Exchange paradigm: people given a good, are reluctant to trade it for another good of similar value The same experiments performed but with TOKENS (currency for future transactions) → WTP = WTA - Suggest that the endowment effect is specific to goods that have perceived valuable & does not apply when the goods are considered mere MEANS OF EXCHANGE ENDOWMENT EFFECT The endowment effect is reduced (equivalently loss aversion is reduced) when: - The own good & the unowned good are close substitutes \the duration of ownership is shorter - Questions are framed in a way that directs sellers to the uses that money from sales can be put to & buyers to the benefits arising from the ownership of the object - Negative emotions are invoked prior to the elicitation of WTA & WTP - Subjects are relatively older or more educated individuals → greater knowledge of the attributes of a product - There is a reduction in the ambiguity about the value of the good & in the cost of gathering information - Explicit price information can reduce the ambiguity about the value & reduce the WTA/WTP disparity Loss aversion is the most accepted explanation → alternative accounts: - Hanemann (1991): neoclassical-based explanation - Reference point - prospect theory (not actual alternative) - Psychological inertia (Gal, 2006) ENDOWMENT EFFECT - REFERENCE POINT ↑An individual at point A, when asked their WTA as compensation to sell X and move to point C, would demand GREATER COMPENSATION for that LOSS than they would be WTP for an EQUIVALENT gain of X units to move themselves to point B → the difference between B-A and C-A would account for the endowment effect; they expect more money while selling but wants to pay less while buying the same good ENDOWMENT EFFECT - PSYCHOLOGICAL INERTIA Psychological interia → people have a natural tendency to hold onto what they have without actively considering alternatives Two basic psychological principles  1. Motives drive behavior 2. Preferences tend to be fuzzy & ill-defined This approach suggests that that the Endowment Effect could arise from the natural inertia of preferring what one already processes → people might not actively fear loss, but are driven by a fundamental tendency to stick with the familiar or owned item (Gal, 2006): argued that behavioral economics has been too concerned with understanding HOW behavior deviates from standard economic models rather than with understanding WHY people behave the way they do MENTAL ACCOUNTING Mental accounting is the set of cognitive operations used by individuals & households to organize, evaluate & keep track of financial activities Individuals have a system on mental accounts → mental accounts are the assignment of transactions to different exogenously created categories & money is not fully fungible across these categories Classical economics assumes that one has a global financial account that simply aggregates all the individual accounts using the assumption of perfect fungibility of money 1. Money is not fungible across mental accounts 2. The individual does not exclusively focus on maximizing overall financial wealth → the objective might also be to limit the size of losses in individual accounts MENTAL ACCOUNTING - PROSPECT THEORY Prospect theory is able to predict many mental accounting phenomena - Multiple gains are segregated → the utility functions for gains under prospect theory is strictly concave (⤵) So if there are several positive gains, they should not be combined → instead, gains should be segregated (EX. savoring a box of chocolates instead of eating it all in one sitting) - Multiple losses are integrated → the utility function under prospect theory is strictly convex (⤴) for losses, so it is optimal to integrate losses (EX. sellers lumping together items for sale) - Mixed net losses and silver lining principe → consider any outcome pair where each outcome is expressed relative to a reference point (b, a) - If b is small relative to a (small gain, large loss) → segregation (silver lining principle) - If b is similar to a → aggregation MENTAL ACCOUNTING - THE LIFE-CYCLE HYPOTHESIS Life-Cycle hypothesis is an economic theory that describes the spending & saving habits of people over the course of a lifetime → States that individuals seek to smooth consumption throughout their lifetime by borrowing when their income is low and saving when their income is high ↑Wealth accumulation is low during youth & old age and high in middle age The LCH assumes that individuals plan their spending over their lifetimes, taking into account their future income They take on debt when they are young, assuming future income will enable them to pay it off They then save during middle age in order to maintain their level of consumption when they retire MENTAL ACCOUNTING Individuals create a system of accounts such that money is not fully tangible across the accounts Disaggregation into the following three main mental accounts: - Current income account - Current assets - Future wealth account A distinguishing feature of these accounts is that some are more tempting to invade than others Accounts that are considered wealth are less tempting than those that are considered as income → in classical economic theory, there is no issue of invading financial accounts It is easiest for the individual to spend from the current income account, more difficult to invade the current asset account & the most difficult to invade the future wealth account MENTAL ACCOUNTING - HEDONIC BENEFITS AND COSTS Coupling (Prelec and Loewenstein, 1998): While enjoying the benefits of a good, consumers could be thinking of the payments. Conversely, while making the payments, consumers could be thinking of the benefits - Alternative methods of payment have different degrees of coupling - EX. credit cards weaken coupling - cash payments strengthen coupling Gifts: provide a direct method of decoupling consumption from payments → separates the payment of the item from the utility gains from buying the item For each transaction, people offset: - Pain of repayments against future consumption (hedonic cost) - Pleasure of consumption against the pain of future repayments (hedonic benefit) Service with a result (EX. hairdresser, doctor, etc.): payment after the service - Durable good: people are happier to pay interest on durable goods (not prepaying) because the pain of paying interest is offset by their anticipated future consumption from the durable good Enjoyment/Nondurable goods that are consumed/enjoyed immediately (EX. vacation, museum, theater): prepayment - There is no future consumption to offset the pain of paying interest → people have a stronger preference for prepaying debt associated with non-durable goods compared with durable goods MENTAL ACCOUNTING - SUNK COST Neoclassical approach → sunk cost incurred irrelevant Experimental evidence indicates that sunk costs DO have an important influence on the actual action taken Sunk cost fallacy: Decisions about an ongoing situation are made based on what has already been invested in that situation When people have previously invested in a choice, they will likely feel guilty/regretful if they do not follow through → the sunk cost fallacy is associated with commitment bias/coherence effect, where people CONTINUE to support their PAST DECISIONS despite new evidence suggesting that isn't the best course of action  - People fail to consider that whatever resources that they have already expended cannot be recovered; people make decisions based on PAST COSTS instead of FUTURE BENEFITS (the only factor that RATIONALLY makes a difference) Sunk cost fallacy is in part due to loss aversion→ describes that the impact of losses feels much worse to people than the impact of gains; people are more likely to avoid losses than to seek out gains The effects of sunk costs can wear off overtime → payment depreciation - EX. Thaler uncomfortable shoes - Once the (depreciated) sunk costs fall below some lower limit, the mental account is closed CONFLICTING CHOICE Cognitive dissonance refers to a situation involving conflicting attitudes, beliefs or behaviors Produces a feeling of mental discomfort leading to an alteration in one of the attitudes, beliefs or behaviors to reduce the discomfort and restore balance People love to be in a cognitive balance → when people make choices, they tend to avoid options that can induce states of dissonance CONFLICTING CHOICE - BURIDAN’S DONKEY Buridan’s donkey (1300s): an illustration of a paradox in philosophy in the conception of free will - Refers to a hypothetical situation where a donkey that is equally hungry & thirsty is placed precisely between a stake of hay & water - the paradox assumes that the donkey will always go whichever is closer → in this context the donkey dies of both hunger & thirst since it cannot make any rational decision - Metaphor of decision paralysis caused by post-decisional cognitive/emotive discomfort FROM CONFLICTING CHOICE TO OVERCHOICE For reasons of cognitive and emotional economy, people tend to avoid conflicts Factors implicated in experiencing discomfort: - Effort - mistake - psychological consequences of having made a mistake These factors have a greater impact as the number of options increases - Every further option requires more effort - Mistakes are more likely as the number of options increases - The psychological consequences of these mistakes are greater OVERCHOICE Overchoice occurs when many equivalent choices are available → making a decisions becomes overwhelming due to the many potential outcomes & risks that may result from making the wrong choice Having too many approximately equally good options is mentally draining (fatigue) because each option must be weighed against alternatives to select the best one The satisfaction of choices by number of options available can be described by an inverted U model - Having no choice results in very low satisfaction - Initially, more choices lead to more satisfaction, but as the number of choices increases it then peaks & people tend to feel more pressure, confusion and potentially dissatisfaction with their choice Larger choice sets can initially be appealing → smaller choice sets lead to increased satisfaction and reduced regret Overchoice & the perception of time: extensive choice sets can seem even more difficult with a limited time constraint The paradox of choice: it is not necessarily true that having more choices in synonymous with greater quality of life or greater happiness → eliminating consumer choices can greatly reduce anxiety for shoppers REGRET Conflicting choice/paradox of choice is strictly related to regret Regret: negative emotion that can be a factor that determines one’s decision Both regret and guilt are evoked by a comparison with the norm → this norm varies depending on whether we are talking about regret or guilt Regret is dominated by the exception to the usual personal behavior → the blame attributed is instead dominated by the conventional norm of average behavior People expect to have greater regret-based emotional reactions when they are the product of action than when they are the product of inaction - EX. people are expected to be happier if they PLAY and WIN than if they DON'T PLAY but they obtain the same amount of money For the same action, it is the deviation from the default/status quo that makes a person feel regret → this tendency favors conventional and risk-averse choices - EX. consumers who are reminded that they may experience regret as a result of their choices are induced to buy branded products DECISIONS AND COGNITIVE DISSONANCE An individual with COHERENT thoughts and behaviors is in a satisfying emotional situation → in the case of INCONSISTENCY (divergent thoughts and behaviors) cognitive dissonance is produced Cognitive dissonance induced discomfort → the individual tries to reduce or eliminate it REDUCTION OF POST-DECISIONAL CHOICE POST-DECISIONAL DISSONANCE AND DISJUNCTION EFFECT Can be explained in terms of post-decisional dissonance → if the outcome of the exam in KNOWN, people will not experience any post-decisional dissonance & already know the “reason” for the holiday Not yet knowing the outcome of the exam causes the different reasons to conflict with each other: co-presence of different post-choice motivations → reduction of emotional conflict Disjunction effect: people are unable to represent all future outcomes ↑The limitation of the cognitive system makes it difficult to understand a priori that, regardless of the outcome, the choice will be to go on holiday This limit is associated with the need to know why people make a certain choice and avoid any decisional conflicts  ERGONOMICS DEFINITION Ergonomics (human factors): the scientific discipline concerned with the understanding of the interactions among humans & other elements of a system/environment Aim: optimize human well-being & overall system performance THE CONTRIBUTION OF DONALD NORMAN Donald Norman: psychologist who emphasizes the role of psychology in design & ergonomics; two key books - The Design of Everyday Things - Emotional Design These two books reflect a progression that can also be observed in the scientific literature on the subject: the shift from focusing on COGNITIVE factors in design to emphasizing the role of EMOTIONAL factors The integration of both functional & emotional aspects is key THE DESIGN OF EVERYDAY THINGS: COGNITIVE ASPECTS Everyday examples: - Prevent potential mistakes in fueling by using a nozzle specific to the type of fuel/car - Incomprehensible signs or error messages: what should I do (parking signs) - Which burner is associated with which knob? THE INTERNET CASE (EX. YOUTUBE) Constant testing & feedback monitoring Jakob’s Law: Changes are also related to the evolution of the appearance of other extremely popular sites (EX. google, facebook) People don't like drastic changes → the presence of periodic innovations is perceived positively, while radical changes to something people are accustomed to using daily (necessitating new learning, slowdowns, errors) are perceived negatively Goal: to keep people engaged for as long as possible and encourage interaction HUMAN ERRORS: DEREK REASON’S CONTRIBUTION Three distinct categories; each type of error has different causes and implications for understanding human behavior & improving decision-making - Mistakes: Involve poor decisions based on incorrect knowledge or reasoning - Lapses: Involve memory failures or attention errors that lead to forgetting to perform a task - Slips: Involve unintended actions that diverge from the intended task, often in routine activities HUMAN ERRORS: MISTAKES Definition: Mistakes occur when a person has incorrect knowledge or beliefs, leading to a poor decision or judgment; they involve a failure in planning or intention Causes: stem from: - Lack of understanding or knowledge about a situation - Misapplication of rules or principles - Poor reasoning or flawed decision-making processes HUMAN ERRORS: LAPSES Definition: Lapses are failures of memory or attention; they occur when a person forgets to carry out an action or loses focus on a task Causes: often related to: - Cognitive overload, stress or fatigue - Interruptions that break a person’s concentration - A failure to retrieve the necessary information from memory HUMAN ERRORS: SLIPS Definition: Slips are unintended actions that occur when a person intends to do one thing but accidentally does another; they are often associated with routine tasks that become automatic Causes: can result from: - Inattention or distraction while performing a task - Automatic behavior that leads to errors, especially when multitasking - Fatigue or decreased cognitive function SUMMARY OF DIFFERENCES AND MANAGEMENT Summary of Differences - Mistakes: Involve poor decisions based on incorrect knowledge or reasoning - Lapses: Involve memory failures or attention errors that lead to forgetting to perform a task - Slips: Involve unintended actions that diverge from the intended task, often in routine activities Management: understanding these distinctions helps in developing strategies to mitigate errors: - For mistakes: training & education can enhance knowledge & decision-making skills - For lapses: reminders and checklists can help ensure important actions are not forgotten - For slips: designing systems that minimize the chance of distraction and error can improve outcomes GENERAL GUIDELINES BY NORMAN TO PREVENT ERRORS Provide good conceptual models through consistent design Develop systems that accommodate human constraints: working memory, attentional resources, reasoning constraints Make options and consequences explicit Assume the occurrence of an error: - Create plans to recover from an error - Do not make an error irreversible Donald Norman distinguishes three levels of processing corresponding to three different types of design: - Visceral level: Rapid & automatic evaluation, not conscious (particularly for basic stimuli) - Behavioral level: The pleasure of effectively using a well-built tool (implicit) - Reflective level: involves cognition, thinking & knowledge of the world (explicit), with conscious & thoughtful aesthetic evaluation Pleasant objets can perform their functions better →ATMs that are more aesthetically pleasing are perceived as easier to use Hypothetical explanation: pleasant objects induce positive mood that promotes creative thinking & cognitive problem-solving - A positive mood induces a state of relaxation that enhances interaction with the object - What is beautiful is also more functional  THE ROLE OF CONTEXT AND TIME ON DECISION-MAKING INTRODUCTION Decisions are never made in isolation - There is always a context in which they are made → how much does the specific context influence the decision? - Time is a form of context - Context effects are everywhere in psychology Definition: Context-driven effect as an over- or under-estimation of a stimulus embedded in a given context compared to the same evaluation task performed in a neutral/different context ATTRACTIVENESS JUDGEMENT (Leding et al. 2015) Context → Exposure to pictures with different levels of attractiveness; may influence judgements of the attractiveness of real people In their first study, they found that when the attractiveness was HIGH, observers rated the photo of a person as LESS attractive; when the attractiveness was LOW, the same person was judged as being MORE attractive In a neutral/no context condition, the judgement would have been made without other pictures ANCHORING Anchoring: A cognitive bias where people rely heavily on the first pieces of information (the “anchor”) they receive when making decisions or judgements; this anchor affects subsequent estimates or choices, even if it is arbitrary or irrelevant - Anchors don't always need to be provided by experimenters in the context of a study; they can be self-generated Field evidence supports anchoring - EX. Immanuel Kant’s birth year - EX. Tversky and Kahneman (1974): subjects were asked to take five seconds to answer either 1*2*...8 or 8*7*...1 due to the time pressure, the first few numbers SERVED AS AN ANCHOR; median answers were 512 and 2250 - EX. Northcraft and Neale (1987): estimated value of properties by estate agents was influenced by the list price, which SERVED AS AN ANCHOR; agents exposed to the high anchor priced the property significantly higher and vise versa Kahneman & Tversky (1974) proposed the “anchoring-and-adjustment heuristic” - An individual bases their initial ideas and responses on one point of information and makes changes driven by that starting point - The anchoring and adjustment heuristic describes cases in which a person uses a specific target number or value as a STARTING POINT (anchor) → subsequently adjusts that information until an acceptable value is reached - OFTEN those adjustments are INADEQUATE & remain TOO CLOSE to the original anchor → problematic when the anchor is very different from the true answer MORAL JUDGEMENT + ASSIMILATION AND CONTRAST EFFECTS DECOY EFFECT The Decoy effect is the phenomenon whereby consumers will tend to have a specific change in preference between two options when also presented with a third option with certain features ↑A = low quality and cheap; B = high quality and expensive → indifference between A and B Db = lower quality and more expensive compared to B; Cb = very high quality and very expensive Sa = very similar to B; slightly lower in quality and lower in price Attraction: The decoy Db is of less quality but more expensive than B → we don't buy Db but it INDUCES us to buy B (with Db, B looks better than A; Attraction Effect occurs when Db is close to B in attribute space, but B is more desirable than D along one or both attributes, causing a shift in preference from A towards B Compromise: B can be made preferable by adding Cb, very expensive and of extreme quality → B looks like a compromise between A and Cb Similarity: Option Sa is very close to B → induces us to buy A FRAMING EFFECT The way a problem is described profoundly influences judgements and choices Risky choice framing: positive frames (gain) generally enhance risk averse responding and negative frames (loss) enhance risky responding Attribute framing: attributes are judged more favorably when labeled in positive terms rather than negative terms Goal framing: negatively framed message emphasizing losses tends to have a greater impact on a given behavior than a comparable positively framed message emphasizing gains QUESTION ORDER EFFECTS Refers to how the order in which questions are presented can influence responses due to changes in context or the respondent’s focus → these effects arise because answering one question can alter the cognitive framework or accessibility of information for subsequent questions CONTEXT EFFECT Refers to how the surrounding context, including external cues, question wording or sequence, influence an individual’s perceptions, decisions or behaviors Context effects can significantly shape the way people respond to stimuli or interpret information, often without conscious awareness - Top-down effects refer to how high-level cognitive processes (prior-knowledge, beliefs, expectations or goals) influence perception, memory and decision-making → highlights how our minds actively interpret sensory information rather than passively receiving it THE ROLE OF TIME: INTERTEMPORAL CHOICE How current decisions affect what options become available in the future - Delaying a reward could increase its value in the future, however, delayed outcomes are less valuable to impatient individuals → this trade-off determines the optimal temporal choice of individuals DISCOUNTED UTILITY (DU) MODELS Discontinuity model refers to theories/frameworks suggesting that sudden changes, breaks or thresholds exist in processes or behaviors, rather than continuous & gradual transitions → highlights the idea that certain changes in behavior, cognition or emotion may NOT follow a linear trajectory but instead occur in a step-linke/abrupt manner Within the rational approach, many models have been proposed → specifically, the DU models; however the psychological foundations of the DU model are fairly weak Samuelson (1937): - Argued that discounted utility model was not meant to perfectly describe real human behavior → it was a simplified framework for understanding intertemporal choices (future rewards or costs are valued less than immediate ones; discounting) - Cautioned that DU models should not be used as a normative standard for welfare analysis; people’s preferences often deviate from the rigid assumptions of the DU model Rational assumption in DU model: time consistency: DU theory assumes time consistency → people’s preferences over time do not change arbitrarily; simplifies the decision-making analysis but is often violated in real-life behavior Violation of time consistency → Read et al (1999): virtues and vices - Vices: exchange small immediate rewards for large delayed costs - Virtues: exchange small immediate costs for large delayed rewards Since the DU model is not able to model the preference reversal between virtues and vices, Read et al. proposed more descriptive models able to predict these kinds of behaviors  THE DUAL PROCESS THEORY OF THOUGHT: THINKING FAST AND SLOW Belief-bias phenomenon: experiment in which the logical validity of an argument and the credibility of the conclusion are manipulated; the question is how acceptable the conclusion is There is a conflict between responses based on a logical process & responses derived from previous beliefs; responses are influenced by BOTH credibility of the proposed conclusion and the validity of the argument DUAL PROCESS-THEORY OF THOUGHT Example: name an animal with starting letter E → solution: elephant - Answer that comes to mind immediately, effortlessly, spontaneously Example: solve anagram “vaeertidebli”→ solution: deliberative - Requires effort & abstract thinking; you are aware not only of the final result, but also of the mental process by which you arrived at the solution Important feature of human cognition: people have the capacity of rapidly formulating answers to questions (autonomous process & faster), but people sometimes engage in deliberate reasoning (requires working memory & slower) processes before responding There is an irrefutable distinction, but this poses serious challenges for our understanding of cognition Differences in terminology: - Fast thinking, intuition, system 1, type 1, associative thinking - Slow thinking, deliberative thinking, system 2, type 2, rule-based thinking, analytical thinking, reflective thinking Fast thinking is aimed at representing the statistics of the external world in a general and flexible way Slow thinking allows you to represent information in an abstract and symbolic way It is not a simple distinction between → Conscious vs unconscious processes - Fast thinking: people are aware of the answer they arrive at, but not the PROCESS that leads them to the answer - Slow thinking: people are aware of the answer they arrive at AND the process that leads them to the answer Rational processes lead to the RIGHT answer from a normative POV VS irrational processes lead to the WRONG answer from a normative POV - Fast thinking: in many cases they lead people to the correct answer, in others they do not (due to biases) - Slow thinking: people can apply normative rules to solve a problem, but sometimes they make mistakes, in other cases they provide the correct answer Type 1: does not require working memory and is autonomous - Some cognitive outputs are determined directly as a result of stimulus-response pairing - Autonomous processes initiate and complete outside of deliberate control Type 2: reasoning in absence of an immediate autonomous response - Cognitive decoupling: the ability to block out context and experiential knowledge