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Prospect Theory
Research Guide

What is Prospect Theory?

Prospect Theory is a descriptive model of decision-making under risk that incorporates loss aversion, reference dependence, and probability weighting, developed by Kahneman and Tversky.

Kahneman and Tversky introduced Prospect Theory in their seminal 1979 paper, critiquing expected utility theory for failing to capture observed choice patterns (Kahneman & Tversky, 2013, 2859 citations). The model features an S-shaped value function concave for gains and convex for losses, with steeper slopes for losses indicating loss aversion. Over 10,000 papers cite this foundational work, applying it across economics and psychology.

Curated Papers

Key Challenges

Why It Matters

Prospect Theory explains investor behavior in the equity premium puzzle, where myopic loss aversion leads to excessive risk aversion for stocks (Benartzi & Thaler, 1993, 1855 citations). Governments use it to design policies like tax loss harvesting and insurance framing to counter loss aversion. In finance, salience effects from Prospect Theory variants predict asset pricing anomalies (Bordalo, Gennaioli, & Shleifer, 2012, 1252 citations).

Key Research Challenges

Cumulative Prospect Theory Extension

Extending Prospect Theory to multi-stage risks requires rank-dependent probability weighting, but empirical tests show inconsistencies with repeated choices (Kahneman & Tversky, 2013). Hertwig et al. (2004, 1674 citations) highlight discrepancies between decisions from description versus experience. Calibration across domains remains unresolved.

Reference Point Identification

Defining stable reference points proves challenging as they shift with expectations and context (Kahneman, 2003, 5413 citations). Frederick (2005, 5117 citations) links cognitive reflection to reference dependence errors. Neuroimaging studies needed for validation.

Loss Aversion Universality Testing

Loss aversion varies across cultures and expertise levels, questioning model universality (Gigerenzer & Brighton, 2009, 1760 citations). Elster (1989, 2013 citations) notes social norms modulate aversion. Field experiments lack scale.

Essential Papers

A perspective on judgment and choice: Mapping bounded rationality.

Daniel Kahneman · 2003 · American Psychologist · 5.4K citations

Early studies of intuitive judgment and decision making conducted with the late Amos Tversky are reviewed in the context of two related concepts: an analysis of accessibility, the ease with which t...

Cognitive Reflection and Decision Making

Shane Frederick · 2005 · The Journal of Economic Perspectives · 5.1K citations

This paper introduces a three-item “Cognitive Reflection Test” (CRT) as a simple measure of one type of cognitive ability—the ability or disposition to reflect on a question and resist reporting th...

Prospect Theory: An Analysis of Decision Under Risk

Daniel Kahneman, Amos Tversky · 2013 · World Scientific handbook in financial economic series · 2.9K citations

This paper presents a critique of expected utility theory as a descriptive model of decision making under risk, and develops an alternative model, called prospect theory. Choices among risky prospe...

Social Norms and Economic Theory

Jon Elster · 1989 · The Journal of Economic Perspectives · 2.0K citations

One of the most persistent cleavages in the social sciences is the opposition between two lines of thought conveniently associated with Adam Smith and Emile Durkheim, between homo economicus and ho...

Contingent Valuation: Is Some Number Better than No Number?

Peter Diamond, Jerry A. Hausman · 1994 · The Journal of Economic Perspectives · 1.9K citations

Without market outcomes for comparison, internal consistency tests, particularly adding-up tests, are needed for credibility. When tested, contingent valuation has failed. Proponents find surveys t...

Myopic Loss Aversion and the Equity Premium Puzzle

Shlomo Benartzi, Richard H. Thaler · 1993 · 1.9K citations

The equity premium puzzle, first documented by Mehra and Prescott, refers to the empirical fact that stocks have greatly outperformed bonds over the last century.As Mehra and Prescott point out, it...

Homo Heuristicus: Why Biased Minds Make Better Inferences

Gerd Gigerenzer, Henry Brighton · 2009 · Topics in Cognitive Science · 1.8K citations

Abstract Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less inf...

Reading Guide

Foundational Papers

Read Kahneman & Tversky (2013) first for model axioms and value function; Kahneman (2003) second for judgment heuristics context; Frederick (2005) for empirical cognitive tests.

Recent Advances

Study Bordalo et al. (2012) for salience extensions; Hertwig et al. (2004) for experience-based decisions; Gigerenzer & Brighton (2009) for heuristic comparisons.

Core Methods

Core techniques: value function v(x) = x^α for gains, -λ(-x)^β for losses; probability weight w(p). Estimated via binary lottery choices and nonlinear regression.

How PapersFlow Helps You Research Prospect Theory

Discover & Search

Research Agent uses searchPapers and citationGraph on 'Prospect Theory loss aversion' to map 2859 citations of Kahneman & Tversky (2013), revealing clusters in finance applications. exaSearch uncovers niche extensions like myopic loss aversion (Benartzi & Thaler, 1993). findSimilarPapers expands to salience theory (Bordalo et al., 2012).

Analyze & Verify

Analysis Agent applies readPaperContent to Kahneman & Tversky (2013) for value function equations, then runPythonAnalysis simulates S-shaped curves with NumPy for loss aversion ratios. verifyResponse via CoVe cross-checks claims against Frederick (2005) CRT data. GRADE grading scores empirical evidence strength.

Synthesize & Write

Synthesis Agent detects gaps in reference dependence applications via contradiction flagging across Kahneman (2003) and Hertwig et al. (2004). Writing Agent uses latexEditText, latexSyncCitations for Kahneman & Tversky (2013), and latexCompile for publication-ready reviews. exportMermaid visualizes prospect theory value functions.

Use Cases

"Replicate myopic loss aversion equity premium simulation from Benartzi & Thaler 1993"

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (NumPy/pandas simulates return distributions) → matplotlib plot of premium puzzle resolution.

"Write LaTeX review of Prospect Theory applications in policy design"

Research Agent → citationGraph → Synthesis Agent → gap detection → Writing Agent → latexSyncCitations (Kahneman 2003) → latexCompile → PDF with diagrams.

"Find code implementations of prospect theory value functions from papers"

Research Agent → paperExtractUrls → Code Discovery → paperFindGithubRepo → githubRepoInspect → verified Python code for S-shaped functions.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ Prospect Theory papers, chaining searchPapers → citationGraph → GRADE grading for structured equity premium report. DeepScan applies 7-step analysis with CoVe checkpoints to verify loss aversion in Hertwig et al. (2004). Theorizer generates extensions from Kahneman (2003) bounded rationality citations.

Try Doxa for Prospect Theory Research

Frequently Asked Questions

What is the core definition of Prospect Theory?

Prospect Theory models decisions under risk with a value function steeper for losses than gains, probability weighting overestimating small probabilities, and reference dependence (Kahneman & Tversky, 2013).

What are key methods in Prospect Theory?

Methods include parametric fitting of S-shaped value functions and inverse-S probability weighting via maximum likelihood on lottery choices. Editing phase precedes evaluation (Kahneman & Tversky, 2000).

What are the foundational papers?

Kahneman & Tversky (2013, 2859 citations) defines the model; Kahneman (2003, 5413 citations) maps bounded rationality; Frederick (2005, 5117 citations) tests via CRT.

What open problems exist in Prospect Theory?

Challenges include dynamic reference points, decisions from experience vs. description (Hertwig et al., 2004), and integration with heuristics (Gigerenzer & Brighton, 2009).