Editing Behavioral Economics (section)

= Behavioral Economics =
A field of study integrating insights from '''[https://wikipedia.org/wiki/Psychology psychology]''' and '''[https://wikipedia.org/wiki/Economics economics]''' to explain how cognitive limitations, emotions, and social factors shape economic decision-making.

== Remembering (Knowledge / Recall) 🧠 ==
=== Core terminology & definitions ===
* '''[https://wikipedia.org/wiki/Bounded_rationality Bounded rationality]''' – The idea that decision-makers have limited cognitive resources, information, and time.
* '''[https://wikipedia.org/wiki/Prospect_theory Prospect theory]''' – A descriptive model of decision-making under risk, highlighting loss aversion and reference dependence.
* '''[https://wikipedia.org/wiki/Heuristic Heuristic]''' – Mental shortcut used to simplify complex judgments.
* '''Nudge''' – A subtle design feature that steers choices without restricting options.

=== Key components / actors / elements ===
* '''Researchers''' – e.g., [https://wikipedia.org/wiki/Daniel_Kahneman Daniel Kahneman], [https://wikipedia.org/wiki/Amos_Tversky Amos Tversky], [https://wikipedia.org/wiki/Richard_Thaler Richard Thaler].
* '''Institutions''' – Behavioral Insights Teams, governmental policy units.
* '''Decision-makers''' – Consumers, investors, policymakers.

=== Canonical models, tools, or artifacts ===
* '''[https://wikipedia.org/wiki/Prospect_theory Prospect theory]'''
* '''[https://wikipedia.org/wiki/Dual_process_theory Dual-process theory]'''
* '''[https://wikipedia.org/wiki/Nudge_(book) Nudge framework]'''

=== Typical recall-level facts ===
* Emerged in the late 20th century as a response to limitations of rational-agent models.
* Applied across finance, public policy, marketing, and health.
* Nobel Prize recognition: Kahneman (2002), Thaler (2017).

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== Understanding (Comprehension) 📖 ==
=== Conceptual relationships & contrasts ===
* Contrasts with '''[https://wikipedia.org/wiki/Rational_choice_theory rational choice theory]''' by emphasizing non-rational influences.
* Relates to '''[https://wikipedia.org/wiki/Behaviorism behaviorism]''' via its focus on observable decisions, yet grounded in cognition.
* Positioned within the broader ecosystem of decision sciences, alongside cognitive psychology and behavioral finance.

=== Core principles & paradigms ===
* People rely on heuristics when facing complexity.
* Preferences are context-dependent rather than fixed.
* Framing effects shift how equivalent information is perceived.

=== How it works (high-level) ===
* '''Inputs''' – Options, incentives, environmental cues.
* '''Cognitive processes''' – Heuristics, biases, reference points.
* '''Behavioral outcomes''' – Choices that often deviate from rational predictions.

=== Roles & perspectives ===
* Policymakers: design interventions that improve welfare.
* Marketers: shape product presentation and pricing.
* Consumers: navigate complex decisions with limited information.

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== Applying (Use / Application) 🛠️ ==
=== "Hello, World" example ===
* A cafeteria rearranges food placement so healthier items appear first, increasing selection rates without restricting choice—a classic nudge.

=== Core task loops / workflows ===
* Identify behavioral bottleneck (e.g., low enrollment).
* Diagnose cognitive bias influencing the behavior.
* Design intervention (default, framing, simplification).
* Test via randomized controlled trial (RCT).
* Iterate and scale successful interventions.

=== Frequently used actions / methods / techniques ===
* Choice architecture design.
* A/B testing and RCTs.
* Behavioral mapping (identifying friction, bias, context).
* Loss-aversion–based messaging.

=== Real-world use cases ===
* Automatic enrollment in retirement savings plans.
* Organ donation defaults (opt-in vs. opt-out).
* Energy usage reports using social comparison.
* Framing health warnings to increase vaccination uptake.
* Pricing bundles in e-commerce.

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== Analyzing (Break Down / Analysis) 🔬 ==
=== Comparative analysis ===
* Versus neoclassical economics: offers higher predictive accuracy for real-world behavior.
* Versus behavioral finance: broader scope; not limited to markets.
* Works best in contexts with clear frictions or bounded rationality; less impactful for highly informed expert decisions.

=== Structural insights ===
* Built on dual-process cognitive architecture (fast/automatic vs. slow/deliberative thinking).
* Relies on systematic cataloging of biases (anchoring, availability, overconfidence).
* Interventions operate by modifying environmental cues rather than preferences.

=== Failure modes & root causes ===
* Overgeneralization of lab results to real-world settings.
* Poorly designed nudges that ignore cultural context.
* Backfire effects when individuals detect manipulation.

=== Troubleshooting & observability ===
* Use RCTs to detect causal impact.
* Track conversion rates, default acceptance rates, time-to-complete metrics.
* Monitor unintended behavior shifts (e.g., reactance).

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== Creating (Synthesis / Create) 🏗️ ==
=== Design patterns & best practices ===
* Defaults that increase desired outcomes without coercion.
* Simplification of user journeys to reduce cognitive load.
* Timely prompts (salience and reminders).

=== Integration & extension strategies ===
* Combine with data science for personalized nudges.
* Integrate into UX design processes.
* Extend through policy instruments such as incentives and regulation.

=== Security, governance, or ethical considerations ===
* Risk of manipulation and autonomy concerns.
* Need for transparency and accountability in public-sector nudging.
* Importance of proportionality and evidence-based justification.

=== Lifecycle management strategies ===
* Pilot → Test → Scale → Monitor → Update.
* Document long-term behavioral sustainability.
* Review interventions when context or incentives shift.

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== Evaluating (Judgment / Evaluation) ⚖️ ==
=== Evaluation frameworks & tools ===
* Metrics: uptake rates, compliance, welfare outcomes.
* Tools: RCTs, quasi-experiments, longitudinal analyses.

=== Maturity & adoption models ===
* Widely adopted in governments (e.g., UK BIT, US Social & Behavioral Sciences Team).
* Increasing integration in corporate product design.
* Barriers include ethical debates and limited practitioner capacity.

=== Key benefits & limitations ===
* Benefits: low-cost interventions, scalable impact, evidence-driven design.
* Limitations: context-specificity, risk of oversimplification, ethical ambiguity.

=== Strategic decision criteria ===
* Use when small frictions meaningfully influence outcomes.
* Avoid when decisions require deep expertise or when stakes are exceptionally high.
* Consider long-term effects and user autonomy.

=== Holistic impact analysis ===
* Influences public health, finance, sustainability, and digital products.
* Shapes default expectations of “user-centered” policymaking.
* Future directions: AI-personalized nudges, cross-cultural validation, stricter ethical frameworks.

[[Category:Behavioral Economics]]
[[Category:Economics]]
[[Category:Psychology]]