Variational analysis and variational rationality in behavioral sciences: stationary traps

TL;DR

This paper applies advanced variational analysis techniques to behavioral science models, focusing on local stationary traps that represent stable positions not worth abandoning, and provides constructive evaluations of these traps.

Contribution

It develops a variational rationality framework for analyzing local stationary traps using generalized differential tools and extremal principles.

Findings

Constructive linear evaluations of stationary traps

Use of subgradients and normals for nonsmooth, nonconvex objects

Application of variational and extremal principles

Abstract

This paper concerns applications of variational analysis to some local aspects of behavioral science modeling by developing an effective variational rationality approach to these and related issues. Our main attention is paid to local stationary traps, which reflect such local equilibrium and the like positions in behavioral science models that are not worthwhile to quit. We establish constructive linear optimistic evaluations of local stationary traps by using generalized differential tools of variational analysis that involve subgradients and normals for nonsmooth and nonconvex objects as well as variational and extremal principles.