Behavioral Science in Policy-Making

The Evolution of Behaviorally Informed Policy-Making

• Behavioural science (Behavioral Insights, BI) has become increasingly relevant in policymaking
• Traditional policy models assume rational decisions
• Decision-making is influenced by heuristics, biases, and environmental factors, which led to integrating behavioral insights into policy design
• Policy effectiveness is improved by understanding how people make decisions
• Policymakers can anticipate irrational behaviour

How Behavioural Science Can Improve Policy

• Nudges and other behavioral interventions can help people make better choices
• Policy outcomes are enhanced by considering actual human behaviour instead of theoretical models
• BI is applied in healthcare, finance, environmental policy, and consumer behaviour
• Examples: Automatic pension enrolment, redesigning tax reminders to increase compliance, and warning labels on cigarette packaging

Evolution of BI in Policymaking: Exploratory Phase (Early 2000s – 2011)

• Initial policy intervention experiments involved adjusting default options and improving warning labels
• Cass Sunstein promoted BI-based policy as head of the Office of Information and Regulatory Affairs (OIRA) in the U.S. in 2009
• The UK Behavioural Insights Team (BIT) was established and led efforts in applying BI to public policy

Evolution of BI in Policymaking: Development Phase (2012-2015)

• BI was applied systematically through experiments in health policies, taxation, and consumer protection
• Governments began testing behavioral interventions using Randomized Controlled Trials (RCTs)

Evolution of BI in Policymaking: Consolidation Phase (2016-Present)

• Behavioural insights became integral to policymaking
• International organizations such as the World Bank and the United Nations, incorporated BI into their policy frameworks
• Many countries developed dedicated BI teams for evidence-based decision-making

Behavioural Science Improves Policymaking

• Influencing different stages of the policy cycle
• Understanding behavioral factors contributing to policy issues (problem identification)
• Collecting data through experiments, surveys, and literature reviews (behavioral research)
• Creating nudges or regulatory policies informed by behavioral science (designing behavioral interventions)
• Conducting RCTs and other experiments to measure effectiveness before full implementation (policy testing)
• Nudging, modifyies choice architecture to encourage better decisions without restricting freedom

Examples of Behavioural Influence

• Automatic pension enrolment increases retirement savings
• Simplified tax letters improve tax compliance
• Health warnings and labels influence consumer behaviour

Challenges and Future of Behavioural Science in Policy

• Late integration in policy processes limits effectiveness
• Over-reliance on nudging overshadows traditional policy tools
• Ethical concerns arise, such as nudges reducing individual autonomy and transparency
• Stronger collaboration with scientists, governments, and the private sector is needed
• Utilize new technologies and big data to understand behavioural patterns
• Develop ethical frameworks to ensure interventions are transparent and fair

BPP Beyond Nudging

• BPP should go beyond nudging to consider collective and organizational behaviour

Criticism of Nudging

• Nudging is a method of subtly guiding individuals toward better choices without restricting their freedom
• Conceptual Criticism - Simplistict to address complex social issues like obesity and poverty
• Methodological Criticism - rely heavily on Randomized Controlled Trials (RCTs) which may not accurately capture real-world complexities

The Limitations of Nudging

• Ethical Criticism - may manipulate behaviour without individuals being aware
• Ideological Criticism - seen as a neoliberal strategy, focusing on individual responsibility rather than addressing systemic problems.

Advanced Version Needed

• Criticisms that have led to integrating behavioural insights across different levels of policymaking

Expanding BPP: Influencing Policymakers and Public Servants

• Traditional BPP focuses on citizens and consumers
• Behavioral insights can improve policy decision-making and government efficiency
• Can improve recruitment and improving risk management in policymaking.

Expanding BPP: Targeting Organisational and Collective Behaviour

• Behavioral insights should not only modify individual choices
• Should also change organisational cultures and group decision-making
• Applies to government agencies, businesses, and public institutions
• Systemic change is often more effective than targeting individuals.

Expanding BPP: Integrating BPP with Traditional Tools

• Should complement regulations, financial incentives, and structural interventions, not replace them
• Addressing childhood obesity requires nudges (e.g., school meal design) and structural changes (e.g., improving food access in low-income areas).

The Future of BPP

• Applied from problem identification to policy design, implementation, and evaluation. Behavioural insights should be embedded in policy development, instead of focusing on one-off interventions

Combining Qualitative and Quantitative Methods for BBP

• RCTs should be complemented with ethnographic research, interviews, and participatory methods to understand behavioural dynamics

Guidelines for Ethical BPP

• Transparency, informed consent, and public participation should be central to behavioral policymaking
• The OECD has proposed ethical frameworks, but these should be independent of government influence

Depoliticizing BPP

• Should not be used as a tool to shift responsibility to individuals
• Should be integrated into multi-level governance strategies

Conclusion

• Broader and more integrated
• Moves beyond nudging individuals, also influence organizations, policymakers, and public institutions
• Uses behavioral insights alongside regulations, incentives, and public engagement
• Ensures ethical and transparent implementation of behavioral interventions

Schlüter et al. (2017). A Framework for mapping and comparing behavioural theories in models

• Social-ecological systems (SES) models study the interactions between humans and their environment
• Help inform resource management and for policy decisions
• Models still rely on the rational actor model is assumed
• Individuals act in a purely self-interested, fully informed, and utility-maximizing manner

Why We Need MoHuB

• Real-world human behaviour is often boundedly rational
• Includes social norms, heuristics, learning, emotions, and risk perception
• Needed a broader integration of behavioural theories is needed to improve SES models
• The MoHuB (Modelling Human Behaviour) framework proposed helps researchers to compare different behavioural theories
• It also helps to Identify key elements of decision-making, and Improve the representation of human behaviour in models

Challenges of behavioural theories and integrating into model

• Includes diversity of theories, many theories that focus on different aspects
• Can be complicated to integrate into SES models

What Causes Incomplete Explanations

• It can focus on individual choices, while others emphasize social interactions
• Many theories describe correlations rather than specifying mechanisms for decision-making.
• Challenges oversimplify human behaviour by using economic assumptions, limiting their usefulness for policymaking

Overview of MoHuB Framework

• Integrate different behavioural theories by decomposing decision-making into key elements
• Framework includes context, the social and environmental setting
• Includes state, individual attributes like needs, knowledge, values

5 More Elements in the Framework

• Perceived Behavioral Options are the choices available
• Perception is how information is gathered
• Evaluation is how information is processed
• Selection is the decision-making process ex. optimization
• Behavior is the final action taken

Framework Usefulness

• Mapping theories onto these elements helps to compare, select, and integrate

Relevant Theory Descriptions

Rational Choice Theory (Homo Economicus)

• Assumes self-interested utility maximization with perfect knowledge
• The process of optimization is choosing best possible option.
• Application is used in economic and environmental policy models
• Limitation is overly simplistic as it doesn't account for bounded rationality or social influences

Bounded Rationality

• Cognitive limits and rely on heuristics when making decisions
• The process involves choosing the first acceptable option (satisficing)
• Modelled through used in agent-based SES models
• It does not include Formalize decision rules

Theory of Planned Behavior (TPB)

• Decision-making is influenced by attitudes, social norms, and perceived control over actions
• Process involves forming intentions, which may lead to action
• Application involves Predicting environmental behaviour ex. recycling
• It is Limited because it doesn't Explicitly model decision selection processes

Habitual Behaviour / Reinforcement Learning

• Process is Automatic unless the behaviour fails, triggering deliberation.
• Modelling includes farming, fishing, or energy use patterns
• It Does not include Specify how individuals switch to deliberative decision-making

Descriptive Norm Theory

• People observe others’ behaviour and tend to conform
• More likely to select behaviours that are socially dominant
• Explains behaviour such as energy conservation or voting.
• It Does not include Account for internal motivation beyond social influence

Prospect Theory

• People weigh potential losses more heavily than gains when making decisions
• Biased risk evaluation influences choice
• Explains risk aversion in climate adaptation and resource use
• Model Requires defining risk perception thresholds

Application of Framework in SES Modeling

• Map existing theories onto decision-making processes
• Identify gaps in current
• Integrate behavioural complexity to help with realistic decision making
• Improve policy predictions by including behavioural complexity.

Conclusions on the Framework

• Helps incorporate diverse behavioural theories into models, making them more realistic and policy-relevant.
• Policymakers can Design more effective interventions, Predict human responses to environmental changes, Improve the sustainability of natural resource management
• Includes bounded rationality, social norms, learning, and risk perception