The value of cognitive foundations for dynamic social theory

It is possible to develop models of social behavior that are predicated on detailed mechanical models of cognition. Cognitively based social models are potentially unified theoretical frameworks that can be used to explain a wide variety of social phenomena. Moreover, if a knowledge representation scheme and a knowledge acquisition scheme are specified in the underlying cognitive model then it is possible to produce a dynamic social model. The resulting social model can thus be used to predict and explain not only conditions for specific behaviors but changes in those behaviors over time.

Constructuralism is a theory of social behavior that rests on a cognitive model. The cognitive model specified has a knowledge representation scheme, knowledge acquisition procedures, and control procedures for shifting cognitive attention. The resulting social model is a dynamic model that can be used to explain both conditions for the occurrence of a behavior and social and individual changes that accrue do to a series of behaviors. The explanatory breadth of the model is illustrated by looking at predictions about a variety of social phenomena including: development of shared knowledge, identical behavior by members of the society, foreign language acquisition, clique formation, civil disobedience, and diffusion of innovative information.     

基于演化博弈的制造企业服务创新扩散研究

制造企业服务化是我国实现制造强国的必经之路,本文以“中国制造2025”战略为背景,通过借鉴创新扩散和演化博弈理论,对社会系统中参与服务创新的相关主体进行分析,构建政府与企业之间演化博弈模型,结合并改进Bass经典创新扩散模型,探究政府规制行为对企业服务创新行为演化及扩散的影响,利用Matlab对复制动态方程和扩散模型进行数学推导和数值仿真分析。结果表明:当满足政府的社会总收益大于其调控成本和各项补贴总额且企业选择服务创新策略时收益的额外增加额大于其实际投入时,系统才会演化至政府调控且企业采纳服务创新的稳定状态;适当增加政府对企业的调控力度、投入补贴和税收补贴有助于推进服务创新在系统中的扩散,其中税收补贴的感知能力最强,投入补贴最弱;将抑制系数引入Bass创新扩散模型,构建互补型、竞争型和替代型三种服务创新的扩散模式,其中竞争型扩散符合中国现阶段市场规律,替代性扩散将成为未来制造企业的发展趋势。     

Inferring social structure from continuous‐time interaction data

Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous‐time methods for modeling such data are based on point processes and directly model interaction “contagion,” whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal‐relational point process models for continuous‐time event data. We characterize interactions between a pair of actors as either spurious or as resulting from an underlying, persistent connection in a latent social network. We argue that consistent deviations from expected behavior, rather than solely high frequency counts, are crucial for identifying well‐established underlying social relationships. This study aims to explore these latent network structures in two contexts: one comprising of college students and another involving barn swallows.     

Lost-sales inventory theory: A review

In classic inventory models it is common to assume that excess demand is backordered. However, studies analyzing customer behavior in practice show that most unfulfilled demand is lost or an alternative item/location is looked for in many retail environments. Inventory systems that include this lost-sales characteristic appear to be more difficult to analyze and to solve. Furthermore, lost-sales inventory systems require different replenishment policies to minimize costs compared to backorder systems. In this paper, we classify the models in the literature based on the characteristics of the inventory system and review the proposed replenishment policies. For each classification and type of replenishment policy we discuss the available models and their performance. Furthermore, directions for future research are proposed.     

The limits of phenomenology: From behaviorism to drug testing and engineering design

It is widely believed that theory is useful in physics because it describes simple systems and that strictly empirical phenomenological approaches are necessary for complex biological and social systems. Here, we prove based on an analysis of the information that can be obtained from experimental observations that theory is even more essential in the understanding of complex systems. Implications of this proof revise the general understanding of how we can understand complex systems including the behaviorist approach to human behavior, problems with testing engineered systems, and medical experimentation for evaluating treatments and the Food and Drug Administration approval of medications. Each of these approaches are inherently limited in their ability to characterize real world systems due to the large number of conditions that can affect their behavior. Models are necessary as they can help to characterize behavior without requiring observations for all possible conditions. The testing of models by empirical observations enhances the utility of those observations. For systems for which adequate models have not been developed, or are not practical, the limitations of empirical testing lead to uncertainty in our knowledge and risks in individual, organizational, and social policy decisions. These risks should be recognized and inform our decisions. © 2015 Wiley Periodicals, Inc. Complexity 21: 181–189, 2016     

Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models,Principles, and Emergent Behaviors

In this paper, we explain the social foraging behavior of E. coli and M. xanthus bacteria and develop simulation models based on the principles of foraging theory that view foraging as optimization. This provides us with novel models of their foraging behavior and with new methods for distributed nongradient optimization. Moreover, we show that the models of both species of bacteria exhibit the property identified by Grunbaum that postulates that their foraging is social in order to be able to climb noisy gradients in nutrients. This provides a connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces.     

Coordination in Dynamic Social Networks Under Heterogeneity

People often make choices or form opinions depending on the social relations they have, but they also choose their relations depending on their preferred behavior and their opinions. Most of the existing models of coevolution of networks and individual behavior assume that actors are homogeneous. In this article, we relax this assumption in a context in which actors try to coordinate their behavior with their partners. We investigate with a game-theoretic model whether social cohesion and coordination change when interests of actors are not perfectly aligned as compared to the homogeneous case. Using analytical and simulation methods we characterize the set of stable networks and examine the consequences of heterogeneity for social optimality and segregation in emerging networks.     

Binariness and rational choice

The concept of numerical representability of preferences together with maximality is at the heart of the concept of rationality embodied in classical optimization models. The difficulty of representing social preferences arises from inherent intransitivities thrown up by democratic voting procedures and by non-binary choice rules which need to be adopted to cope with these intransitivities. An alternative (weaker) concept of representability is developed and it is shown that this concept can partially accommodate intransitivity and non-binariness. ‘Weak transitivity’ and ‘weak binaries’ conditions are provided and it is shown that these conditions are necessary and sufficient for ‘weak representability’. While the weak transitivity condition would be violated by social aggregation procedures, the non-binary functions used by social choice theorists do indeed satisfy the condition of ‘weak binariness’.     

Evolution of shame as an adaptation to social punishment and its contribution to social cohesiveness

Feelings of shame are common among humans although shameless individuals do not seem to be handicapped in achieving social success in life. What then is the adaptive value of shame? How can shame have evolved? Here I simulate shame as the emotion that induces an increase in pro‐social behavior after receiving social punishment. Simulations with the agent‐based model, Sociodynamica, show that shame is evolutionary stable in a context of individual selection, without the need for including group selection as an evolutionary force. The adaptive advantage of shame is based on the fact that it increases flexibility to the shameful individual, allowing it to act selfish if the probabilities of being punished are low and achieving a reduction in the costs of social punishment when frequent punishment is likely. The results show that shame, together with pro‐social punishment and social cooperation, produce a fluctuating dynamics of social cooperation, achieving long periods where the populations stabilizes pro‐social behavior interspersed with periods where selfish behavior predominates. This temporal stabilization of pro‐social behavior might provide societies with sufficient time to build institutions that might stabilize sustainable pro‐social behavior. © 2008 Wiley Periodicals, Inc. Complexity, 2008     

Cellular dynamics: How individual decisions lead to global urban change

Models of micro-macro dynamics based on the bifurcation paradigm have helped gain some deep insights into the behavior of certain kinds of complex systems, including urban systems. Here an alternative kind of model will be presented, the scope and qualitative behavior of which appears to be similar to that of the models of the bifurcation type, although it is arguably more general, not being restricted to real-valued functions. This alternative framework is derived from discrete modeling theory and generalizes the notion of ‘cellular automata’. In the application outlined here, the large-scale evolution of urban residential densities is represented as the result of a large number of interdependent investment decisions made by individual developers. The exact relation of this model with a bifurcation model of the same process remains an interesting theoretical question.     

DOES A RENEWABLE NATURAL RESOURCE USUALLY HAVE MANY NEAR‐OPTIMAL ALLOCATION PATHS?

ABSTRACT. Utilizing a deterministic single‐species computational renewable natural resource model, it is found that there exist many dynamic allocation paths near‐optimal to the efficient path, where the latter maximizes conventionally‐defined social welfare. Some alternative paths involve substantial trading of surplus over time among market participants and hold different implications for income distribution. Universality of findings cannot be asserted, but the results are intuitively plausible and suggest that multiple near‐optimal allocation paths may exist in related models as well. Existence of many near‐optimal paths holds significant implications for theory and policy.     

Friendship Networks Through Time: An Actor-Oriented Dynamic Statistical Network Model

We propose a class of actor-oriented statistical models for closed social networks in general, and friendship networks in particular. The models are random utility models developed within a rational choice framework. Based on social psychological and sociological theories about friendship, mathematical functions capturing expected utility of individual actors with respect to friendship are constructed. Expected utility also contains a random (unexplained) component. We assume that, given their restrictions and contact opportunities, individuals evaluate their utility functions and behave such that they maximize the expected amount of utility. The behavior under consideration is the expression of like and dislike (choice of friends). Theoretical mechanisms that are modelled are, e.g., the principle of diminishing returns, the tendency towards reciprocated choices, and the preference for friendship relations with similar others. Constraints imposed on individuals are, e.g., the structure of the existing network, and the distribution of personal characteristics over the respondents. The models are illustrated by means of a data-set collected among university freshmen at 7 points in time during 1994 and 1995.     

Open versus closed loop capacity equilibria in electricity markets under perfect and oligopolistic competition

We consider two game-theoretic models of the generation capacity expansion problem in liberalized electricity markets. The first is an open loop equilibrium model, where generation companies simultaneously choose capacities and quantities to maximize their individual profit. The second is a closed loop model, in which companies first choose capacities maximizing their profit anticipating the market equilibrium outcomes in the second stage. The latter problem is an equilibrium problem with equilibrium constraints. In both models, the intensity of competition among producers in the energy market is frequently represented using conjectural variations. Considering one load period, we show that for any choice of conjectural variations ranging from perfect competition to Cournot, the closed loop equilibrium coincides with the Cournot open loop equilibrium, thereby obtaining a ‘Kreps and Scheinkman’-like result and extending it to arbitrary strategic behavior. When expanding the model framework to multiple load periods, the closed loop equilibria for different conjectural variations can diverge from each other and from open loop equilibria. We also present and analyze alternative conjectured price response models with switching conjectures. Surprisingly, the rank ordering of the closed loop equilibria in terms of consumer surplus and market efficiency (as measured by total social welfare) is ambiguous. Thus, regulatory approaches that force marginal cost-based bidding in spot markets may diminish market efficiency and consumer welfare by dampening incentives for investment. We also show that the closed loop capacity yielded by a conjectured price response second stage competition can be less or equal to the closed loop Cournot capacity, and that the former capacity cannot exceed the latter when there are symmetric agents and two load periods.     

Models of social contagion

This paper argues that social choice from among more than two feasible alternatives should not be based on social choice from two‐alternative subsets. It considers in some detail the case where one alternative ties or beats every other alternative on the basis of simple majorities, and raises the question of whether such an alternative should be chosen. A condition of ‘stochastic unanimity’, introduced in this context, is shown to be incompatible with the simple majority rule when it can apply. This new condition plus a consideration of ties leads into a brief discussion of the use of individual expected utility in social choice theory.     

A stochastic model for assessing the utility of chance

We consider the problem of assessing the utility of chance. One approach is based on offering a decision maker a choice between receiving a fixed chance and a gamble. We investigate theoretical properties of a class of models for the probability of preference of the gamble. We point out an alternative model that models behavior near the boundary more realistically.     

Fuzzy relations for the analysis of traders’ preferences in an information market game

In this paper we focus on preference and decision data gathered during a computer-supported information market game in which 35 students participated during seven consecutive trading sessions. The participants’ individual preferences on the market shares are collected to calculate a collective preference ranking using the Borda social choice method. Comparing this preference ranking to the shares’ actual market ranking resulting from the participants’ trading, we find a statistically significant difference between both rankings. As the preferences established by market behavior cannot be adequately explained through a social choice rule, we propose an alternative explanation based on the herd behavior phenomenon where traders imitate the most successful trader in the market. Using a decision analysis technique based on fuzzy relations, we study the participants’ rankings of the best share in the market during 7 weeks and compare the most successful trader to the other traders. The results from our analysis show that a substantial number of traders is indeed following the market leader.     

DYNAMIC BEHAVIOURS OF AN AUTONOMOUS STAGE-STRUCTURED COMPETITIVE SYSTEMS WITH TOXIC EFFECT

It is known that somebody''s behavior (decision) in a stochastic social network may be influenced by that of his (or her) friends. In this paper, we consider two stochastic social network game models (a) and (b) which can be defined respectively by two different utility functions. Some sufficient conditions for the existence of Nash equilibrium (NE) of the two network game models are obtained by analyzing the different effort relation between a player and his (or her) neighbors.     

Leaving us in tiers: can homophily be used to generate tiering effects?

Substantial evidence indicates that our social networks are divided into tiers in which people have a few very close social support group, a larger set of friends, and a much larger number of relatively distant acquaintances. Because homophily—the principle that like seeks like—has been suggested as a mechanism by which people interact, it may also provide a mechanism that generates such frequencies and distributions. However, our multi-agent simulation tool, Construct, suggests that a slight supplement to a knowledge homophily model—the inclusion of several highly salient personal facts that are infrequently shared—can more successfully lead to the tiering behavior often observed in human networks than a simplistic homophily model. Our findings imply that homophily on both general and personal facts is necessary in order to achieve realistic frequencies of interaction and distributions of interaction partners. Implications of the model are discussed, and recommendations are provided for simulation designers seeking to use homophily models to explain human interaction patterns.