Human Behavior, Cultures, and Societies - Class 4 PDF

Human behavior, cultures and societies Class 4 Nicolas Baumard Jean-Baptiste André TA: Marius Mercier Why so much evolution? In cognitive science, the focus is on how individuals process information, regardless of what the ultimate goal might be. This involves: Detecting stimuli: How we perceive the world through sensory inputs. Analyzing signals: How our brain processes those inputs into meaningful patterns (e.g., in linguistics, understanding speech or written language). Making decisions: Choosing a course of action based on the analysis, often framed in terms of how efficiently or accurately we can achieve a task. In cognitive science, the goal itself doesn't necessarily need to be defined—what matters is understanding how decisions are made, regardless of why an individual might choose one behavior over another. In social science, the emphasis shifts to what individual do because social phenomena are the aggregate consequences of what people do, how their interact with each others. But to understand why people behave the way they do, we need to understand their goals such as safety, food, friendship, love, reputation, status, information. And to understand their goals, we need evolution. Evolutionary theory is essential because the goals individuals pursue (whether consciously or unconsciously) are the produced of natural selection. To understand that, we need a good understanding of natural selection. Key notions so far life history trade-off adaptive (or phenotypic) plasticity Environment of Evolutionary Adaptedness (EEA) ultimate and proximate conflict of interest (take an adaptive perspective-taking) Evolutionary Stable Strategy (ESS) Intrinsic and extrinsic motivation what drives the rate of evolutionary change within a species? If so, would that imply that animals with higher rates of evolutionary change in turn have, as a trade-off, overall decreased phenotypic plasticity? The rate of evolutionary change primarily depends on generation time what drives the rate of evolutionary change within a species? If so, would that imply that animals with higher rates of evolutionary change in turn have, as a trade-off, overall decreased phenotypic plasticity? The rate of evolutionary change primarily depends on generation time, species with short generation evolve faster And, yes, phenotypic plasticity will evolve in species where generation time is long in comparison with the rate of environmental change Why is that after all? Why is that after all? Because phenotypic plasticity is not useful for species that can adapt genetically very fast! Hum Why is that after all? Because phenotypic plasticity is not favored at the individual level, when generation time is fast relative to environmental change When the generation time is short relative to the rate of environmental change, there are enough generations within each stable environmental period for specialized genotypes to be favored. These specialized genotypes tend to outperform plastic genotypes because they are ﬁne-tuned to the current conditions, while plastic genotypes incur a cost due to maintaining ﬂexibility (the costs of plasticity). As a result, selection consistently favors specialized genotypes in each environmental phase. Evolutionarily stable strategies Feeding or checking for predators Resource intake + - Trade-o ff + - Risk of predation Optimal strategy Probability of Resource survival intake % of time spent checking Evolution by natural selection will lead to an optimal compromise between the two objectives (survive predation and acquire resources) This compromise allows understanding the logic of individual decisions Feeding or checking for predators with others The “optimal” time one should spend checking vs. feeding depends on what others do! If everyone else is vigilant “I’m safe, I can afford to eat!” If no-one else is vigilant “I’m in danger, I’d better be careful” Natural selection rarely acts on individuals independently of others How high should a tree grow? Well, it depends on what others do Should one escalate the conﬂict with a rival? Well, it depends on what others do Should a virus invest in RNA replication or translation? Well, it depends on what others do Should a lioness hunt? Well, it depends on what others do Etc. Evolutionary Game Theory No single “optimal” strategy can be deﬁned in such cases Game theory was introduced into biology to deal with these situations A payoff matrix Payoff to player A B Checking Feeding A Checking Safe but hungry Safe but hungry Feeding Safe and nourished Dead A payoff matrix with numbers Payoff to player A Mathematical B representation of the Checking Feeding marginal effect of the A game on the reproductive success of player A Checking 0 0 Feeding 1 -10 The Nash Equilibrium John F. Nash The Nash Equilibrium A strategy S is a Nash equilibrium iff: No alternative strategy is strictly better than S, when playing against S The Nash Equilibrium It’s not optimal in the absolute sense It’s optimal against itself, i.e., it is unbeatable Looking for stable endpoints of evolution (e.g. % time Trait value checking) Biological generations A trait value such that, when this trait value is ﬁxed in the population, then "it won’t change anymore”. Looking for stable endpoints of evolution (e.g. % time Trait value checking) Biological generations A trait value such that, when this trait value is ﬁxed in the population, no rare mutant can increase in frequency The Resident/Mutant reasoning/obsession Imagine that a given strategy would be entirely ﬁxed in the population : the resident What determines whether a rare mutant is favored (i.e. increases in frequency) in this resident population? The Resident/Mutant reasoning/obsession The mutant will live in a world full of residents S The mutant can increase in frequency iff it is strictly better than S, when playing against S We are looking for a Nash equilibrium! We want S such that, No alternative strategy is strictly better than S, when playing against S (i.e., in a world full of S) Evolutionarily Stable Strategy John Maynard Smith Evolutionarily Stable Strategy is an Evolutionarily Stable Strategy (ESS) iff: When is entirely ﬁxed in a population, there is no rare mutant able to increase in frequency by natural selection The population has reached a stable endpoint ESS ≅Nash equilibrium (+a second condition, unimportant in most cases) Hyper-vigilant resident Less vigilant mutant is favored When the resident is hyper-vigilant, a mutant with low vigilance is favored Low-vigilance resident More vigilant mutant is favored When the resident is not vigilant at all, a mutant with higher vigilance is favored A given resident is ﬁxed in the population Mutants that are less vigilant than the resident are favored Fitness of the rare Fitness of the resident mutant Vigilance of a rare mutant Resident vigilance is not ESS A given resident is ﬁxed in the population Mutants that are more vigilant than the resident are favored Fitness of the rare Fitness of the resident mutant Vigilance of a rare mutant Resident vigilance is low is not ESS Neither mutants that are more vigilant, nor mutants that are less vigilant are favored Fitness of the rare Fitness of the resident mutant Vigilance of the rare mutant Resident vigilance is intermediate is ESS Some game examples Prisoner’s dilemma Payoff matrix of the prisoner’s dilemma Payoff to player A B Cooperates (remains Defects silent) (confess) A Cooperates (remains 10 -2 silent) Defects 15 0 (confess) What’s the “optimum” strategy? Payoff to player A B Cooperates (remains Defects silent) (confess) A Cooperates (remains 10 -2 silent) Defects 15 0 (confess) What’s the socially optimum strategy? Payoff to player A B Cooperates (remains Defects silent) (confess) Social optimum A Cooperates (remains 10 -2 silent) Defects 15 0 (confess) What’s the ESS? Payoff to player A B Cooperates (remains Defects silent) (confess) A Cooperates (remains 10 -2 silent) Defects 15 0 (confess) ESS (and strict Nash) Cooperates Defects (remains silent) (confess) Cooperates 10 -2 (remains silent) Defects 15 0 (confess) ‣ The average payoff at ESS (0) is much lower than what it would be if individuals were cooperating (10) ‣ The ESS is not the social optimum The snowdrift game The snowdrift game Payoff to player A B Helps (clearing snow) Defects (free-rides) A Helps (clearing snow) Free after an effort Free after a big effort Defects (free-rides) Free with no effort Night in the cold The snowdrift game Payoff to player A B Helps (clearing snow) Defects (free-rides) A Helps (clearing snow) 6 2 Defects (free-rides) 8 -4 What’s the optimum strategy? Payoff to player A B Helps (clearing snow) Defects (free-rides) A Helps (clearing snow) 6 2 Defects (free-rides) 8 -4 What’s the social optimum? Payoff to player A B Helps (clearing snow) Defects (free-rides) Social optimum A Helps (clearing snow) 6 2 Defects (free-rides) 8 -4 What’s the ESS? Payoff to player A B Helps (clearing snow) Defects (free-rides) Helping is favored if partner defects A Defection is favored if partner helps Helps (clearing snow) 6 2 V V There is no ESS (in pure strategy) Defects (free-rides) 8 -4 Negative Frequency-dependence Cooperators favored Defectors favored when rare when rare Frequency 0 1 of cooperators Intermediate frequency of cooperators at equilibrium So, now, what’s the problem with group functionalism? E.g., Birds limit the number of offspring they produce to avoid overusing resources, ensuring that there are enough resources available for the species to survive in the long term. The Resident/Mutant obsession The question we must always ask ourselves is: Is this proposed strategy/behavior/trait evolutionarily stable? Or (more precisely): Can it resist invasion by all possible mutants? Say, everyone limits reproduction (or whatever) (e.g. only 2 offspring a year) ⦿⦿ ⦿ ⦿⦿ Good quality ⦿ ⦿ environment ⦿ ⦿ ⦿ The good environment beneﬁts everyone! What if a mutant appeared that reproduces slightly more (e.g. 3 offspring a year) ⦿ ⦿ ⦿ ⦿⦿ The quality of the ⦿ ⦿ environment is slightly reduced ⦿ ⦿ ⦿ The environment is the same for everyone! All individuals receive the same beneﬁt from the environment (b), but only "self-restraining" individuals pay the cost of restraining (c) Self-restraining individuals have a lower ﬁtness b b-c Frequency of the mutant strategy Time (generation) self-restrain is unstable, i.e. it can be invaded by mutants ⇔ self-restrain is not an Evolutionarily Stable Strategy (ESS) Not for the good of the species Not for the good of the group Not for the good of the population Because the good of the group is NOT an ESS! Let’s practice (Wooclap) Further examples Groupthink / survival of the species Lions kill some cubs to keep the population size in check. Wolves establish strict territories to prevent overhunting and maintain sustainable prey populations. Swans are monogamous because stable family units help the species by raising healthier offspring. Plasticity does not evolve when generation time is short because it is not useful for species that can adapt fast to their environment Etc. Correct inclusive ﬁtness explanations Male peacocks grow bright feathers because it increases their chances of attracting mates Male lions kill cubs of other males so that the females will come into estrus sooner Wolves defend their territories to ensure they and their kin have exclusive access to prey Worker ants refrain from reproducing because their genes are passed on indirectly by helping the queen, who shares most of their genetic material, to reproduce. Lions hunt in groups because it improves their chances of catching prey, which directly increases the expected amount of meat that each individual lion will get. Sexual reproduction evolves because it increases genetic diversity, enhancing the ability of individuals to adapt to changing environments and resist diseases. Senescence evolves due to the declining force of natural selection with age, as individuals invest early in reproduction, and late-life deterioration has less impact on individual ﬁtness. Etc. So how can cooperation be an ESS? The three mechanisms allowing the evolution of cooperation 1. Kin selection: When cooperation beneﬁts someone Can genetically related (e.g. parental care, eusociality in ants, explain altruism etc.) 2. Byproduct beneﬁts: When cooperation happens to be in the cooperator’s direct interest for an automatic reason (e.g., collective hunting, group augmentation, etc.) Can “only” explain 3. Conditional cooperation: When cooperation triggers a Mutualistic cooperation conditional response that eventually beneﬁts the cooperator (e.g., reputation-based cooperation, partner choice, etc.) Cooperation and phenotypic plasticity/psychology Evolutionary game theory models are always very simple, considering “stupid” organisms hardwired to either Cooperate, Defect or, at best, play simple strategies like TFT. Yet, human cooperation is not hard-wired and stupid. Human cooperation is the outcome of phenotypic plasticity (a.k.a, psychology): people cooperate in highly conditional ways, depending on the speciﬁc situation. What is adaptive is the decision-making system that determines when to cooperate or not – the reaction norm. The shape of this reaction norm is predicted from the results of simple evolutionary game theory models. - Humans cooperate conditionally based on information about kinship, adjusting their willingness to cooperate depending on how closely related they are to others. - They cooperate based on the cooperation of others, avoiding situations where they are consistently exploited without any reciprocal beneﬁt. - They condition their cooperation on the reputational signiﬁcance of the situation: they take into account the likelihood of being observed and the reputational cost if they are. Question 1: Real altruism? - how do we explain these anonymous donations (bearing in mind that there is no question of reputation)? - Both paper portray “sharing” or “altruism” as a “competitive” strategy, but how about real and authentic altruism - which also exist (especially in certain communities, like in buddhist philosophies) ? Question 1: Real altruism? The intrinsic motivation to be altruistic, even when it appears disconnected from gaining a direct or immediate benefit, can be explained through evolutionary psychology by understanding the interplay between proximate mechanisms (immediate psychological motivations) and ultimate causes (evolutionary functions) of behavior. 1. Proximate vs. Ultimate Explanations Proximate explanations focus on the immediate psychological motivations driving behavior. In the case of altruism, people might feel good when helping others due to the release of neurochemicals like oxytocin or dopamine, which create a sense of satisfaction and well-being. This creates the subjective experience of intrinsic motivation—the feeling that we want to help others simply because it feels rewarding, independent of external rewards or reputation. Ultimate explanations look at why such behaviors evolved in the first place. From an evolutionary perspective, altruism has evolved because, over time, it has increased reproductive fitness. People who helped others, especially in socially interdependent groups, gained benefits in terms of reputation, alliances, and reciprocal support. Therefore, even if the immediate motivation feels intrinsic, it serves the ultimate function of increasing an individual's long-term success. Natural selection favors traits and behaviors that improve an individual's chances of survival and reproduction. For altruism to evolve, it didn’t require that individuals consciously think about gaining reputation or long-term benefits. Instead, natural selection shaped psychological mechanisms that make people feel good when helping others, as this encourages altruistic behaviors that were beneficial for the group and, indirectly, for the altruist. Over time, those mechanisms became deeply ingrained as emotional or intrinsic motivators. Question 1: Real altruism? The risk of appearing not genuine when performing altruistic acts can significantly undermine both the immediate and long-term benefits of altruism, particularly in the context of indirect reciprocity and reputation management. 2. Reputation management Altruism that is perceived as calculated—where others believe the individual is only helping for self-serving reasons—can lead to a loss of trust. If people suspect that an altruistic act is being performed with ulterior motives (e.g., to gain favor, power, or future rewards), they may view the altruist as manipulative rather than truly cooperative. This can cause individuals to: Withdraw cooperation: People are less likely to reciprocate or cooperate with someone they believe is not genuinely altruistic, which limits the potential benefits of altruism. Monitor the individual more closely: Individuals may start scrutinizing the person’s future actions for signs of selfishness or manipulation, making it harder for the person to regain trust. Spread negative perceptions: In tight-knit social groups, reputations spread quickly. A person perceived as insincere may develop a reputation as someone who cannot be trusted, even in situations unrelated to altruism. Question 1: Real altruism? Engineering the "Altruistic" Robot Imagine you want to build a robot that people perceive as truly altruistic, so it gains a positive reputation and becomes trusted within a community. If you program the robot to focus on maximizing short-term benefits—always acting altruistically only when it directly benefits its reputation (e.g., only helping when others are watching)—it will appear manipulative. People would quickly catch on that the robot is not helping out of true concern or generosity, but rather to serve its own self-interest. As a result, they would mistrust the robot and might even avoid cooperating with it. Question 1: Real altruism? The Solution: Build Intrinsic Altruism To avoid this, you would need to engineer the robot to behave as though it cares intrinsically about the well-being of others, even when no direct reputational reward is at stake. This would involve: 1. Genuine altruism algorithms: Program the robot to help others even when no one is watching, or when there is no immediate gain. This makes its actions appear authentic and not driven by self-interest. 2. Long-term strategy: The robot should prioritize long-term trust and cooperation over short-term gains. It should not constantly seek immediate social rewards but should help because it "feels" it is the right thing to do (even though we, as engineers, know it’s part of the design). In this analogy, the robot is like a human whose altruism evolved not to calculate each individual act for short-term benefits, but to behave in ways that appear genuine and build long-term trust. Similarly, humans aren’t constantly calculating how every act of kindness will affect their reputation, but instead, they are driven by emotions like empathy, compassion, or guilt, which evolved to encourage cooperative behavior. Over time, this authentic behavior fosters trust and reputation naturally. Question 2 - how would certain acts of altruism between species be accounted for? For example, whales and dolphins protecting humans from sharks or humans saving wild animals at great potential cost to themselves Question 2 In evolutionary psychology, acts of altruism between species, such as whales and dolphins protecting humans from sharks or humans rescuing wild animals, can be interpreted through the concept of evolutionary mismatch. This concept refers to situations where behaviors that evolved to solve problems in the ancestral environment may be less well-suited or even maladaptive in the modern world, but still manifest due to underlying psychological mechanisms. For whales and dolphins, protecting humans from sharks can also be viewed as a potential evolutionary mismatch. These animals have complex social structures and cooperative behaviors within their own species, possibly evolved for predator defense. When dolphins protect humans, they might be responding to cues they interpret as distress or vulnerability, mistakenly extending their evolved protective behaviors to members of another species. The ancestral environment of these marine mammals did not include humans, but their evolved behaviors for aiding group members could be misapplied when they encounter humans in apparent danger. Question 2 The evolutionary psychological approach to culture Evolution Psychology Culture (natural selection) (individual preferences) (cultural products) Parenting 79 Question 3 - Why would there be free ride behaviour in animals if they are not capable of the same reasoning capacities as humans (and therefore not able to calculate the benefits-costs of involvement in cooperation)? Question 3 From an evolutionary perspective, cooperation does not require advanced reasoning or complex cognitive skills, even though it might seem like something that requires the ability to calculate costs and benefits. Many animals, including social insects, engage in cooperative behaviors through simple rules or instincts that evolved because they increased the chances of survival and reproduction over time. Take social insects like ants, bees, and termites, for example. They exhibit highly cooperative behaviors, such as dividing labor or defending their colony, without any need for reasoning or complex decision-making. These behaviors are encoded in their biology—natural selection has shaped them to perform these roles because it benefits the group (and their own genetic success) without the need for individual calculations. Similarly, free-riding behavior, where an individual benefits from the cooperation of others without contributing, can arise because natural selection also favors individuals that maximize their own benefits when possible. In some situations, free-riders can exploit cooperative systems without needing to reason about it. Over time, evolutionary pressures can lead to strategies that minimize or punish free-riders, ensuring that cooperation remains stable in the population. So, while humans might reason about costs and benefits, many animals rely on evolved instincts and patterns of behavior that lead to cooperation, as these strategies have been shaped by evolution, not by conscious reasoning. - Question 4 - Normally people don’t stop being generous toward others when in a relationship. What benefit is there in that, from an evolutionary point of view? Question 5 - If pro-social behaviors have a reproductive goal, why do they appear before the age at which reproduction is possible? Question for an essay - what about “toxic” relationships? Why do some people remain dependent on their partner when the costs of maintaining and nurturing the relationship far outweigh the benefits? Question for an essay - Could we think of religious rituals to be a kind of ’filter’ that gathers a reputation for cooperation and helps identify who else is cooperative? Be cause attending such rituals and having a lifestyle devoted to the religion could be seen as a sign of commitment to a group.

Human Behavior, Cultures, and Societies - Class 4 PDF

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