Dynamic models of appraisal networks explaining collective learning

Abstract

This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality. Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science.

Criteria for asymptotic clustering of opinion dynamics towards bimodal consensus

“…Finally, inequalities (11) and (12), imply that: m (∞). As a consequence, only two possibilities arise, namely: m(x e ) = 1 (consensus:…”

“…The F-J model and its variants have been largely analysed in several scenarios (i.e. [4][5][6][7]11,8] just to cite a few). Another specific and more realistic feature introduced in models of opinion dynamics is the notion of bounded-confidence interaction, that is the possibility of a state-dependent interaction where two agents may influence each other if and only if the difference between their opinions is smaller than a given confidence bound.…”

A survey on the analysis and control of evolutionary matrix games

“…Although beyond the scope of this article, the shifting of equilibrium points has been considered in the context of stochastically stable equilibria of evolutionary snowdrift games [73]. Also in a systems and control setting, RD has been invoked to study task assignment among team members [74], virus spread control [75], extremumseeking controllers [76] and direct reciprocity [77].…”

Passivity-based Analysis and Design for Population Dynamics with Conformity Biases

“…Indeed, some significant issues in society are categorized as discrete choice: route selection in transportation networks [14], choice of energies [15], and water distribution [16], for example. Meanwhile, when we deal with systems including large scale of population, it should be required to consider decision models as a population [17]. Discrete choice behavior by large populations is well-addressed in evolutionary game theory, in which various types of population dynamics have been presented, e.g., logit dynamics [18], [19], Smith dynamics [20] and pairwise comparison dynamics [21].…”