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Behavioral Economics

Behavioral Economics ArXiv ID: ssrn-245828 “View on arXiv” Authors: Unknown Abstract Behavioral Economics is the combination of psychology and economics that investigates what happens in markets in which some of the agents display human limitati Keywords: Behavioral Economics, Prospect Theory, Cognitive Biases, Heuristics, General (Economics) Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a theoretical and conceptual survey of behavioral economics, focusing on high-level ideas like bounded rationality and limits of arbitrage with minimal mathematical formalism or empirical data. It lacks backtests, datasets, or implementation details, positioning it as a philosophical/theoretical discussion rather than a quantitative trading strategy. flowchart TD A["Research Goal:<br>Understand deviations from<br>rational economic models"] --> B{"Methodology"} B --> C["Theoretical Modeling<br>e.g., Prospect Theory"] B --> D["Experimental Design<br>Labs & Field Studies"] C --> E["Data Inputs:<br>Psychological Heuristics &<br>Bias Observations"] D --> E E --> F["Computational Processes:<br>Agent-Based Simulation<br>& Probability Weighting"] F --> G{"Key Findings/Outcomes"} G --> H["Prospect Theory<br>Loss Aversion & Reference Dependence"] G --> I["Identified Cognitive Biases<br>e.g., Anchoring, Framing"] G --> J["Policy Implications<br>Nudges & Market Regulation"]

October 23, 2000 · 1 min · Research Team

Behavioral Economics

Behavioral Economics ArXiv ID: ssrn-245733 “View on arXiv” Authors: Unknown Abstract Behavioral Economics is the combination of psychology and economics that investigates what happens in markets in which some of the agents display human limitati Keywords: Behavioral Economics, Prospect Theory, Cognitive Biases, Heuristics, General (Economics) Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper involves established financial models and deterministic thresholds but lacks statistical backtesting or empirical datasets. The focus is on practical application and optimization within existing frameworks, fitting the Street Trader profile. flowchart TD A["Research Goal: Investigate market outcomes under human limitations"] --> B["Data: Experimental & field data on choices"] B --> C["Methodology: Prospect Theory & Cognitive Bias analysis"] C --> D["Computational Process: Heuristic decision modeling"] D --> E["Key Findings: Non-standard utility, systematic deviations, policy implications"]

October 12, 2000 · 1 min · Research Team