Behavioral Aspects of Organizational Learning and Adaptation

In this paper, I seek to understand the behavioral basis of higher organizational learning and adaption as a teleological dynamic equilibrium process to decipher the underlying psycho-physiological aspects of individual cognitive learning related to organizational adaption. Dynamics of cognitive learning has some differential paths within the neural circuitry which follows certain patterns that leads to individual as well as organized evolution in course of a learning process. I undertake a comparative analysis of human cognitive and behavioral changes and the active mechanisms underlying animal behavior and learning processes to understand the differential patterns of these adaptive changes in these two species. Cognitive behavioral learning processes have certain economic perspectives which help an individual to attain efficiency in workplace adaptation and in learning which however, the individual when being part of an alliance, ember positive influence on the society or organization as a whole. Comparatively, in primates, I review some empirical evidences drawn from chronological studies about cognitive behavioral learning process and adaptation as well as the presence of the capacity of making attributions about mental states, which exists in rudimentary form in chimpanzees and apes. Following this, I apply the outcomes of the findings on different aspects of human cognitive and adaptive behavioral learning-induced evolutionary changes and how human beings are able to exploit the presence of these additive advantages under cluster settings.Animal behavior, cognitive economics, motivational energy, neural adaptation, neuroscience, Organizational learning, organizational adaptation, teleological process

Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics

A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all real-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both issues. In this, PD theory not only provides a principled formulation of bounded rationality and a set of new types of mean field theory in statistical physics. It also shows that those topics are fundamentally one and the same.Comment: 17 pages, no figures, accepted for publicatio

General self-motivation and strategy identification : Case studies based on Sokoban and Pac-Man

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we use empowerment, a recently introduced biologically inspired measure, to allow an AI player to assign utility values to potential future states within a previously unencountered game without requiring explicit specification of goal states. We further introduce strategic affinity, a method of grouping action sequences together to form "strategies," by examining the overlap in the sets of potential future states following each such action sequence. We also demonstrate an information-theoretic method of predicting future utility. Combining these methods, we extend empowerment to soft-horizon empowerment which enables the player to select a repertoire of action sequences that aim to maintain anticipated utility. We show how this method provides a proto-heuristic for nonterminal states prior to specifying concrete game goals, and propose it as a principled candidate model for "intuitive" strategy selection, in line with other recent work on "self-motivated agent behavior." We demonstrate that the technique, despite being generically defined independently of scenario, performs quite well in relatively disparate scenarios, such as a Sokoban-inspired box-pushing scenario and in a Pac-Man-inspired predator game, suggesting novel and principle-based candidate routes toward more general game-playing algorithms.Peer reviewedFinal Accepted Versio

Beyond rationality: images as guide-lines to choice

In this paper I will discuss a representation of the decision-making process that is based on images production, and exploitation, in order to propose theoretical refinement of the link between decisional behaviour and mental images in economic contexts. To do so, I will start by shortly presenting Beach and Mitchell' s model (1987). I will then extend it to economics, by comparing its implications with those deriving from two more traditional approaches, both the neoclassical one and those recently from Simon' s bounded rationality theory: The most significant consequences of an image-based model appear to be: i) wider cognitive foundations for the decision process; ii) the need to extend the concept of rationality by enlarging its relationship with that of imagination; iii) a new and fuller "image" of the economic agent.

Empirical models, rules, and optimization

This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm's hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise.Decision-making. ,econometric models ,TMD ,

Spectacular pehnomena and limits to rationality in genetic and cultural evolution

In studies of both animal and human behaviour, game theory is used as a tool for understanding strategies that appear in interactions between individuals. Game theory focuses on adaptive behaviour, which can be attained only at evolutionary equilibrium. Here we suggest that behaviour appearing during interactions is often outside the scope of such analysis. In many types of interaction, conflicts of interest exist between players, fueling the evolution of manipulative strategies. Such strategies evolve out of equilibrium, commonly appearing as spectacular morphology or behaviour with obscure meaning, to which other players may react in non-adaptive, irrational way approach, and outline the conditions in which evolutionary equilibria cannot be maintained. Evidence from studies of biological interactions seems to support the view that behaviour is often not at equilibrium. This also appears to be the case for many human cultural traits, which have spread rapidly despite the fact that they have a negative influence on reproduction