Ergodicity-breaking reveals time optimal decision making in humans

Area of Science:

• Decision Science
• Behavioral Economics
• Dynamical Systems Theory

Background:

• Ergodicity links expectation values and time averages, crucial for understanding decision-making under uncertainty.
• Optimizing wealth over time requires agents to adapt utility functions to specific environmental dynamics (e.g., linear for additive, logarithmic for multiplicative).
• Current decision theories lack clarity on whether humans exhibit time-optimal behavior across diverse dynamics.

Purpose of the Study:

• To investigate how additive versus multiplicative gamble dynamics influence human risky choice.
• To determine if human utility functions adapt to gamble dynamics in line with time-optimal predictions.
• To explore the role of ergodic principles in grounding decision-making theories.

Main Methods:

• Experimental comparison of human risky choices under controlled additive and multiplicative gamble dynamics.
• Analysis of observed utility functions and risk aversion levels in response to different dynamics.
• Comparison of empirical findings with predictions derived from time-optimal strategies and ergodic theory.

Main Results:

• Human utility functions are significantly modulated by gamble dynamics, a finding not fully explained by existing theories.
• Risk aversion increases under multiplicative dynamics, closely approximating values that maximize long-term wealth growth.
• Behavior aligns with time-optimal strategies, suggesting an implicit adaptation to ergodic principles.

Conclusions:

• Human decision-making demonstrates adaptive risk tolerance influenced by environmental dynamics.
• Findings support the application of ergodic theory to understand and predict optimal decision-making under uncertainty.
• There is a need to integrate ergodic considerations explicitly into future theories of human decision-making.