Metamimetic Games : Modeling Metadynamics in Social Cognition

Imitation is fundamental in the understanding of social system dynamics. But the diversity of imitation rules employed by modelers proves that the modeling of mimetic processes cannot avoid the traditional problem of endogenization of all the choices, including the one of the mimetic rules. Starting from the remark that human reflexive capacities are the ground for a new class of mimetic rules, I propose a formal framework, metamimetic games, that enable to endogenize the distribution of imitation rules while being human specific. The corresponding concepts of equilibrium - counterfactually stable state - and attractor are introduced. Finally, I give an interpretation of social differentiation in terms of cultural co-evolution among a set of possible motivations, which departs from the traditional view of optimization indexed to criteria that exist prior to the activity of agents.Social cognition, imitation, cultural co-evolution, differentiation, reflexivity, metacognition, stochastic game theory, endogenous distributions, metamimetic games, counterfactual equilibrium.

Metamimetic Games: Modeling Metadynamics in Social Cognition

Imitation is fundamental in the understanding of social systems' dynamics. But the diversity of imitation rules employed by modelers proves that the modeling of mimetic processes cannot avoid the traditional problem of endogenization of all the choices, including the one of the mimetic rules. Starting from the remark that metacognition and human reflexive capacities are the ground for a new class of mimetic rules, we propose a formal framework, metamimetic games, that enables to endogenize the distribution of imitation rules while being human specific. The corresponding concepts of equilibrium — counterfactually stable state — and attractor are introduced. Finally, we give an interpretation of social differenciation in terms of cultural co-evolution among a set of possible motivations, which departs from the traditional view of optimization indexed to immutable criteria that exist prior to the activity of agents.Social Cognition, Imitation, Cultural Co-Evolution, Differentiation, Reflexivity, Metacognition, Stochastic Game Theory, Endogenous Distributions, Metamimetic Games, Counterfactual Equilibrium

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

Credit assignment in multiple goal embodied visuomotor behavior

The intrinsic complexity of the brain can lead one to set aside issues related to its relationships with the body, but the field of embodied cognition emphasizes that understanding brain function at the system level requires one to address the role of the brain-body interface. It has only recently been appreciated that this interface performs huge amounts of computation that does not have to be repeated by the brain, and thus affords the brain great simplifications in its representations. In effect the brain’s abstract states can refer to coded representations of the world created by the body. But even if the brain can communicate with the world through abstractions, the severe speed limitations in its neural circuitry mean that vast amounts of indexing must be performed during development so that appropriate behavioral responses can be rapidly accessed. One way this could happen would be if the brain used a decomposition whereby behavioral primitives could be quickly accessed and combined. This realization motivates our study of independent sensorimotor task solvers, which we call modules, in directing behavior. The issue we focus on herein is how an embodied agent can learn to calibrate such individual visuomotor modules while pursuing multiple goals. The biologically plausible standard for module programming is that of reinforcement given during exploration of the environment. However this formulation contains a substantial issue when sensorimotor modules are used in combination: The credit for their overall performance must be divided amongst them. We show that this problem can be solved and that diverse task combinations are beneficial in learning and not a complication, as usually assumed. Our simulations show that fast algorithms are available that allot credit correctly and are insensitive to measurement noise

Does bounded rationality lead to individual heterogeneity? The impact of the experimentation process and of memory constraints

In this paper we explore the effect of bounded rationality on the convergence of individual behavior toward equilibrium. In the context of a Cournot game with a unique and symmetric Nash equilibrium, firms are modeled as adaptive economic agents through a genetic algorithm. Computational experiments show that (1) there is remarkable heterogeneity across identical but boundedly rational agents; (2) such individual heterogeneity is not simply a consequence of the random elements contained in the genetic algorithm; (3) the more rational agents are in terms of memory abilities and pre-play evaluation of strategies, the less heterogeneous they are in their actions. At the limit case of full rationality, the outcome converges to the standard result of uniform individual behavior.bounded rationality; genetic algorithms; individual heterogeneitybounded rationality; genetic algorithms; individual heterogeneity

A Conceptual Model of Investor Behavior

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis.behavioral finance;financial decision making;agent-based artificial financial markets;cognitive modeling;investor behavior

Creating New Ventures: A review and research agenda

Creating new ventures is one of the most central topics to entrepreneurship and is a critical step from which many theories of management, organizational behavior, and strategic management build. Therefore, this review and proposed research agenda is not only relevant to entrepreneurship scholars but also other management scholars who wish to challenge some of the implicit assumptions of their current streams of research and extend the boundaries of their current theories to earlier in the organization’s life. Given that the last systematic review of the topic was published 16 years ago, and that the topic has evolved rapidly over this time, an overview and research outlook are long overdue. From our review, we inductively generated ten sub-topics: (1) Lead founder, (2) Founding team, (3) Social relationships, (4) Cognitions, (5) Emergent organizing, (6) New venture strategy, (7) Organizational emergence, (8) New venture legitimacy, (9) Founder exit, and (10) Entrepreneurial environment. These sub-topics are then organized into three major stages of the entrepreneurial process—co-creating, organizing, and performing. Together, the framework provides a cohesive story of the past and a road map for future research on creating new ventures, focusing on the links connecting these sub-topics

Strategies for Advancing Evolution

As detailed in The Evolutionary Manifesto and Evolution’s Arrow, an understanding of the large-scale processes that govern the evolution of life is capable of making sense of human existence. This evolutionary worldview is revealing that we are not just meaningless accidents in a universe that is indifferent to our existence. Instead it shows that humanity has a critical role to play in the future evolution of life on this planet and in the universe. But what specifically do we need to do to advance the evolutionary process and ensure that humanity successfully fulfils its role in the future evolution of life? This paper uses the direction of evolution to answer this fundamental question. It identifies the next great steps in evolution on Earth, and devises strategies that will help actualize those steps