A Study of Atomic Mobility Game with Uncertainty Under Prospect Theory

Concetti Chiave

Incorporating prospect theory into a mobility game to study travel behavior under uncertainties.

Sintesi

Introduction to the study of a mobility game with uncertainty.
Incorporation of prospect theory to model travel behavior.
Formulation of a mobility game for travelers in transportation networks.
Characterization of equilibrium and best-fit parameters.
Analysis of prospect theory and its application in decision-making.
Modeling framework for routing games with splittable traffic.
Mathematical formulation and theoretical properties analysis.
Derivation of an upper bound for error and existence of Nash Equilibrium (NE).
Conclusion on the study's findings and future research directions.

Statistiche

None

Citazioni

"Prospect theory has laid down the theoretical foundations to study biases in human decision-making."
"Humans compare outcomes to known expected utility, deviating from rational choice theory."
"Routing games allow investigation into efficiency and congestion impact."

Approfondimenti chiave tratti da

A Study of an Atomic Mobility Game With Uncertainty Under Prospect Theory

by Ioannis Vasi... alle arxiv.org 03-20-2024

https://arxiv.org/pdf/2303.17790.pdf

Domande più approfondite

How can optimization techniques be applied to analyze convex-concave piecewise non-linear problems?

Optimization techniques can be effectively applied to analyze convex-concave piecewise non-linear problems by leveraging methods such as sequential convex programming or cutting plane methods. These techniques break down the complex problem into smaller, more manageable subproblems that are easier to solve iteratively. Sequential convex programming involves approximating a non-convex function with a sequence of convex functions and optimizing them sequentially. On the other hand, cutting plane methods add linear constraints iteratively to approximate the non-linear function until an optimal solution is reached. By utilizing these optimization approaches, researchers and analysts can efficiently tackle challenging convex-concave piecewise non-linear optimization problems.

What are the implications of incorporating taxation mechanisms in mobility systems under prospect theory?

Incorporating taxation mechanisms in mobility systems under prospect theory can have significant implications on traveler behavior and system efficiency. Taxation mechanisms introduce financial incentives or penalties based on travelers' decisions, aiming to influence their choices towards desired outcomes like reducing congestion or promoting sustainable modes of transportation. Under prospect theory, individuals evaluate gains and losses subjectively relative to a reference point, affecting how they respond to taxes or subsidies.
Implications:

Behavioral Response: Prospect theory suggests that individuals may exhibit loss aversion tendencies when faced with potential losses due to taxes, leading them to alter their travel behavior.
Equity Concerns: Taxation could impact different socio-economic groups disproportionately based on their risk attitudes and reference points.
System Efficiency: Properly designed tax schemes aligned with prospect-theoretic principles could optimize system efficiency by influencing route choices and mode preferences.
Revenue Generation: Taxes collected from mobility users could be reinvested in infrastructure improvements or public transport enhancements.

Overall, incorporating taxation mechanisms within mobility systems under prospect theory requires careful consideration of behavioral responses, equity concerns, system efficiency goals, revenue generation strategies while ensuring alignment with broader policy objectives.

How can artificial intelligence incentivize prospect-theoretic travelers while maintaining system efficiency?

Artificial intelligence (AI) offers innovative solutions for incentivizing prospect-theoretic travelers while simultaneously maintaining system efficiency in mobility networks:

Personalized Incentives: AI algorithms can analyze individual travel patterns and preferences derived from prospect theory models to offer personalized incentives tailored towards each traveler's risk attitude and reference point.

Dynamic Pricing Strategies: AI-powered dynamic pricing models adjust fares based on real-time demand fluctuations considering travelers' subjective perceptions of utility under uncertainty.

Gamification Techniques: Implementing gamified elements through AI-driven platforms encourages desirable travel behaviors among users by rewarding compliance with recommended routes or modes.

4 .Predictive Analytics: Leveraging predictive analytics capabilities of AI enables proactive identification of potential congestion hotspots allowing for timely interventions through targeted incentives.
By harnessing the power of artificial intelligence technologies intelligently integrated with insights from prospect theory models, it becomes possible not only to motivate travelers effectively but also enhance overall system performance and user satisfaction levels within mobility ecosystems efficiently maintained at high standards..