Skip to main content
Risk Aversion in Behavioral Economics Dataset

Risk Aversion in Behavioral Economics Dataset

USD276.69
Adding to cart… The item has been added

What does the Risk Aversion in Behavioural Economics Dataset include?

The Risk Aversion in Behavioural Economics Dataset includes 1,501 prioritised and categorised data points across experimental findings, behavioural scenarios, utility function parameters, and real-world applications. Delivered as downloadable Excel and CSV files, it contains coded risk preference measurements, annotated case studies, benchmarking metrics against established economic theories, and decision architecture templates, all structured for immediate use in modelling, analysis, or programme design.

What if your risk models are missing the hidden drivers of human decision-making? Without accurate, empirically grounded data on risk aversion in behavioural economics, your analyses may misrepresent real-world choices, leading to flawed strategies, failed interventions, or ineffective product designs. The Risk Aversion in Behavioural Economics Dataset is a comprehensive, research-backed self-assessment dataset containing 1,501 prioritised, categorised, and evidence-linked data points that quantify how individuals perceive and respond to risk across contexts. This dataset enables you to build more accurate behavioural models, benchmark decision-making patterns, and validate interventions with confidence, so you’re not relying on assumptions, but on proven behavioural patterns.

What You Receive

  • A fully structured Excel and CSV dataset with 1,501 empirically supported data points on risk aversion, enabling immediate integration into statistical models, simulations, or machine learning pipelines
  • 247 coded behavioural scenarios across investment, health, consumer choice, and public policy domains, each mapped to experimental outcomes and utility function parameters
  • 85 calibrated risk preference measurements derived from peer-reviewed studies, including certainty equivalents, probability weighting functions, and loss aversion coefficients
  • 67 annotated case studies showing real-world applications of risk aversion principles in financial advising, insurance design, and behavioural public policy
  • 12 comprehensive taxonomy frameworks categorising risk aversion by demographic, cultural, cognitive bias, and situational triggers, enabling targeted segmentation and intervention design
  • 50 benchmarking metrics for comparing observed behaviour against established norms in expected utility theory, prospect theory, and cumulative prospect theory
  • 30 decision architecture templates showing how framing, defaults, and feedback loops alter risk-taking behaviour in controlled environments
  • Instant digital access to all files, ready for analysis in R, Python, SPSS, or Tableau, with clear metadata documentation and variable definitions

How This Helps You

You’re not just collecting data, you’re building decision intelligence grounded in human behaviour. With this dataset, you can rapidly test hypotheses about risk sensitivity, calibrate agent-based models, or validate nudges in choice architecture. Without access to systematically organised risk aversion metrics, you risk designing programmes based on rational actor assumptions that fail in practice, wasting time, budget, and credibility. Organisations that overlook behavioural realism face lower engagement, suboptimal product uptake, and regulatory scrutiny when risk disclosures don’t align with actual comprehension. By using empirically validated patterns from this dataset, you reduce model error, strengthen evidence-based design, and enhance predictive accuracy in high-stakes decisions. This is how you move from theoretical insight to operational impact, while avoiding the cost of trial-and-error implementation.

Who Is This For?

  • Behavioural scientists and economists building predictive models of decision-making under uncertainty
  • Policy designers requiring empirical benchmarks for risk communication and disclosure effectiveness
  • Financial product developers needing realistic parameters for customer risk tolerance assessments
  • Consultants creating behavioural diagnostics for organisations implementing nudge-based strategies
  • Data analysts integrating psychological realism into customer segmentation or risk scoring engines
  • Academic researchers conducting meta-analyses or replication studies on risk preference stability
  • AI and simulation teams training algorithms on human-consistent risk response patterns

Choose this dataset to ensure your work reflects how people actually behave, not just how they’re assumed to. This is the professional standard for rigorous, reproducible, and applicable behavioural economics research.

!