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Multigame Dynamics: Structures and Strategies
Abstract
The dominant strategy among game theorists is to pose a problem narrowly, formalize that structure, and then pursue analytical
solutions. This strategy has achieved a number of stylized insights, but has not produced nuanced game-theoretic solutions
to larger and more complex issues such as extended international historical conflicts, or the detailed assessment of variegated
policy alternatives. In order to model more complex historical and policy-oriented processes, it has been proposed that a
broader computational approach to game theory that has the potential to capture richer forms of social dynamics be used, namely
the ‘multigame.’ In the multigame approach there are multiple games each of which is open, prototypical, implicit, reciprocal,
positional, variegated and historical. When later implemented, the multigame approach will offer the potential to rigorously
model complex international historical conflicts and variegated policy alternatives that, heretofore, typically required qualitative
analysis.
... The actors are permitted to play multiple games simultaneously. This multiplicity means that the actor must take interactions among relevant games and other players into account [16]. Gamified IS can be interpreted as a multigame, i.e. a system of two games, where one game is a serious game (i.e., target IS) and another game is an entertainment game (i.e., gamification layer in target IS), where an action in the serious game leads to a reward in the entertainment game. ...
Gamification is the use of elements of game design and mechanics in serious contexts for enhancing the value of a service or a product for its users. We describe psychological foundations and social motives for gamification, its principles and concepts, game rules (mechanics) and elements of game as well as techniques and patterns of gamification. As a case study, we analyze gamification of a Trogon Project Management System. For evaluation of gamified interface, we propose to use WCAG 2.0 rules, adapted for evaluating color contrast of game layer interfaces, and System Usability Scale (SUS) to evaluate usability of gamification.
... Researchers have analyzed institutions by using the multi-game framework that illustrates individual social behavior within multiple institutions (Gagen 2000(Gagen , 2003Sallach et al. 2010). How such a multi-game framework changes the equilibrium structures and dynamics of elemental games has also been analyzed (Hashimoto 2009;Hashimoto and Aihara 2009). ...
Previous game-theoretic studies of institutions have viewed institutional changes as either exogenous changes in game form or changes in the game equilibrium through exogenous shocks. Both views of institutions are static and cannot express endogenous changes in institutions. The latter approach states that multiple institutional systems can be kept stable through institutional complementarity and that the changes in institutional systems only arise from exogenous shocks that are sufficiently large to overturn such complementarity. However, they cannot account for the aspects of competition and co-existence where multiple institutions change their relative frequency through endogenous changes. In this article, we model the ecological systems of institutions, as an extensive synthesis of replicator dynamics and evolutionary games, to describe institutional systems that evolve phylogenetically associated with changes in population structure or a pool of rules as replicators, which corresponds to a gene pool. A mathematical model of rule ecosystem dynamics describes rule dynamics wherein multiple rules change their relative weights through evaluations by individuals. In this model, the concept “a meta-rule = an individual value consciousness” is introduced for the rule evaluation. Depending on the setting of the meta-rule, the dynamics of the game rules and individual strategic rules change. We can thus comprehend the endogenous formation, alteration, and extinction (i.e., the evolution of institutions) through the interactions among the game rules as well as those between the game rules and strategic rules. Many other studies focus on how rational individuals select strategies to maximize their payoffs without considering the bounds of rationality. Even when considering these, individual cognitive frameworks and values are typically given. By contrast, this study assumes that individuals have internal rules that express cognitive frameworks and values as meta-rules and analyzes the dynamic interactions between institutions, as social external rules at the meso level, and strategic rules and value consciousness, as individual internal rules at the micro level. In our model, institutional changes do not arise as game equilibria (i.e., players’ selection of strategies in a game), but rather as the rise and fall of game forms, as various rules, in multi-games based on a meta-rule. This view is based on an evolutionary approach where socio-economic evolution is considered to be a selection of rules and institutions rather than that of individuals or their strategies. We discuss the implications of the model of institutional ecosystems on the description of the socio-economy and its evolutionist institutional design.
... Other frameworks based on Multi-Agent Systems (MAS) and game theory have been widely used to incorporate sociocultural influences. Multigames [41] is an interesting framework for representing social interactions in complex scenarios such as economic games. The advantages of multigames include explicit representation of multiscalar aspects of social systems and incorporation of social aspects such as roles. ...
Social Network Analysis (SNA) is a powerful tool for analyzing social phenomena that is based on studying how actors are connected or interact with each other. All Social Networks (SNs) are inherently embedded in particular cultures. However, the effect of cultural influence is often missing from SNA techniques. Moreover, to incorporate culture, modeling approaches have to deal with inaccurate, unrealistic, and incomplete cultural data. In order to address this problem, we propose a generic approach to systematically represent culture in the form of relevant factors and relationships, while leveraging relevant social theories, and to infuse them into SNs in order to obtain more realistic and complete analyses. Using two sets of experiments, we validate the effectiveness of our approach and demonstrate the significant advantages obtained through culturally infused SNA.
This State of the Art Report focuses on applications of Artificial Inteligence (AI) that are relevant to DoD and other governmental agencies that share similar goals, such as the U.S. Department of Homeland Security and the Intelligence Community. HDIAC collaborated with subject matter experts to obtain interpretations on relevant applications of AI to DoD and other agencies that could similarly incorporate AI into their operations. Discussions narrowed on relevant applications that have been in use for the past three years as well as advances that could become commercialized within the next 18–24 months. Given the vast amount of information, this report is not all-inclusive and is only a survey of prominent developments.
Category theory and, more specifically, topos categories provide a more expressive type of mathematical modeling and, thereby, open the door to social models that are both rigorous and expressive. The present analysis draws upon four views of topos categories to construct a rich model of a logic based on recognition theory. The topos initially provides support for: (1) set theory (and classical logic), (2) topological regions dualism. The resulting structures support: (3) a local intuitionist logic that can vary by situated circumstances and actor types, and (4) the specification of regions that are defined by finely-differentiated classifiers. These tools construct an integrated topos category of social recognition that supports diverse forms of local logic. The latter are then explored regarding their contributions to a mathematical model of social conflict. Categorial analysis of historical patterns, as well as a complementary simulation model, is used to illustrate the advantages of such an approach.
Randall Collins has introduced a simplified model of emotional dynamics in which emotional energy, heightened and focused by interaction rituals, serves as a common denominator for social exchange: a generic form of currency, except that it is active in a far broader range of social transactions. While the scope of this theory is attractive, the specifics of the model remain unconvincing.
After a critical assessment of the currency theory of emotion, a field model of emotion is introduced that adds expressiveness by locating emotional valence within its cognitive context, thereby creating an integrated orientation field. The result is a model which claims less in the way of motivational specificity, but is more satisfactory in modeling the dynamic interaction between cognitive and emotional orientations at both individual and social levels.
This article interleaves two themes: (a) the prospective role of agent simulation in developing a high-consensus, rapid-discovery social science and (b) the use of situation modeling to illustrate how the interaction of social theory and agent architectures may contribute toward such a development. The first theme suggests that computational techniques have the potential to contribute to the advancement of broadly shared social science goals. The second theme uses a relatively specialized epistemological focus, the social situation as a focus of attention and action, to illustrate how agent architectures based on that focus may contribute to scientific progress and, in particular, how they may need to evolve. Situationist epistemology is invoked to convey the important role of ontological assumptions and architectural frameworks in social scientific modeling but also to advance situational models within multilevel social theory.
Expressive Order introduces affect control theory to lay readers of sociology, and additionally guides sociology specialists into the theory's deep structure. Briefly, affect control theory proposes that individuals shape their social interactions so that emerging impressions reinforce sentiments about salient identities, behaviors, and settings. Emotions signal how the process of confirming sentiments is going for each individual. The theory explains behaviors, emotions, social labeling, and personality attributions in a wide variety of social contexts-including intimate relations, work-world interactions, courtrooms, and international relations. Part 1 of the book provides a plain-language exposition of the theory, along with numerous interpretive analyses of everyday situations. This is engaging and provocative reading for anyone interested in social relations, including undergraduates in and out of the social sciences. Part 2 presents the mathematical derivations that define sentiment-confirming behavior, labeling, attribution, and emotion. The mathematical solutions, conjoined with understandings about social institutions, are the basis of the theory's explanations. The derivations clarify the theory's assumptions and reasoning, as only mathematics can. Part 3 of Expressive Order describes the research program associated with the theory and the computer simulation software that is used in research. This part of the book offers a jump start for individuals wishing to use affect control theory in their own research. © 2007 Springer Science+Business Media, LLC All rights reserved.
On May 11, 1945, three days after the cessation of hostilities against Germany, President Harry S. Truman ordered a drastic cutback in lendlease aid to the Soviet Union. The following day, civilian and military officials, zealously executing the directive, halted loadings in port and even recalled several ships at sea bound for Russia. Truman’s order naturally evoked loud protests from the Soviets. The Grand Alliance was disintegrating. Joseph Stalin interpreted the reduction of lend-lease as an American attempt to extort political concessions through economic pressure.
Agent-based modeling and simulation (ABMS) - a way to simulate a large number of choices by individual actors - is one of the most exciting practical developments in business and government modeling since the invention of relational databases. It represents a new way to understand data and generate information that has never been available before - a way for businesses and governments to view the future and to understand and anticipate the likely effects of their decisions on their markets, industries, and territories. It thus promises to have far-reaching effects on the way that businesses and governments in many areas use computers to support practical decision-making. This book has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features and advantages of ABMS to other people; and third, to teach readers how to actually implement ABMS by building agent-based simulations. It aims to be a complete ABMS resource and also provides a complete collection of ABMS business and government applications resources.
Recognition of the radial structure of concepts, and the role that prototype conceptual structures play in human reasoning, has been a vital innovation within cognitive science during the last half of the twentieth century (Lakoff 1987; Rosch and Lloyd 1978). However, the larger implications of this insight have yet to be fully realized. The concept of a cognitive prototype was generated largely from empirical research, so one way of describing the undeveloped potential, is that the utilization of prototype concepts within active, dynamic reasoning processes has yet to be adequately explored. Such an oversight is understandable, since research strategies that would identify reasoning processes in depth are more difficult to design than studies that document prototypes as a prevalent form of psychological (primarily recall) data.
This article describes answerability, a fundamental component of social reason and action. “Holding answerable” and “being answerable” are characterized in terms of their roles in the drama of human relations, and our general tendency to anticipate answerable, rather than ethical, behavior in situations that are ethically problematic is discussed.
Economic ActionThe concept of economic actionReligious Ethics and Economic RationalityThe Market: Its Impersonality and EthicClass, Status, PartyEconomically Determined Power and the Status OrderDetermination of Class Situation by Market SituationStatus HonorParties
Serial reciprocity exists when people reciprocate for what they have received—for example, from a parent, a friend, a mentor, a stranger, a previous generation—by providing something to a third party, regardless of whether a return is also given to, or makes its way back to, the original giver. To understand serial reciprocity as reciprocity, this article delineates the general features of the serial type of reciprocity and outlines two general situations in which serial reciprocity provides a viable option—the only or the most appropriate option—for reciprocal return. It also argues for a more fundamental rethinking of reciprocity in general. A more cognitive and cultural perspective on reciprocity is proposed that focuses on the meaning of exchanges and treats reciprocity as a socially constructed element of a culturally available repertoire. This can better account for the existing serial type of reciprocity. The article concludes with suggestions for empirical research.
Contenido: 1) Teoría de la probabilidad; 2) Teoría de la decisión bayesiana; 3) Teoría de juegos: conceptos básicos; 4) Eliminación de estrategias dominadas; 5) Estrategia pura de equilibrios de Nash; 6) Estrategia mixta de equilibrios de Nash; 7) Modelos del agente principal; 8) Juegos de señalización; 9) Juegos reiterativos; 10) Estrategias evolutivamente estables; 11) Sistemas dinámicos; 12) Dinámica evolutiva; 13) Economías de Markov y sistemas dinámicos estocásticos; 14) Cuadro de los símbolos; 15 Respuestas.
J.C. Gower's similarity algorithm (1971) is flexible in its
ability to classify objects with diverse types of attributes. The author
extends Gower's algorithm to handle composite objects using semantic
data modeling. The resulting composite classification algorithm, which
can be integrated with a knowledge base, facilitates the definition and
use of prototype concepts
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Prototype classification and logical classification New Trends in Conceptual Representation: Challenges to Piaget’s Theory?
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Understanding Conflict and War: The Conflict Helix
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Cultural Realism: Strategic Culture and Grand Strategy in Chinese History
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