Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theoretic approach to collective decision making, agent-based simulations were conducted to investigate how human collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing non-trivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multi-level decision making are discussed.Comment: 27 pages, 5 figures, 2 tables; accepted for publication in Complexit

Rationality, preferences and irregular war

We suppose that civilians under threat prefer certain situations within a context of irregular war and endangered survival; they will prefer those situations associated with greater probabilities of survival. Using lexicographical preferences and belief systems, we have shown that civilians will choose not to remain in situations having a lower probability of survival. Linking into social networks allows for shorter deliberation processes, lower decision costs and faster convergence towards collective decision-making. Civilian displacement thus becomes the outcome of a rational decision-making procedure.Survival

Poverty and public celebrations in rural India

The author examines the paradox of very poor households, spending large sums on celebrations. Using qualitative, and quantitative data from South India, the author demonstrates that spending on weddings, and festivals can be explained by integrating an anthropological understanding of how identity is shaped in Indian society, with an economic analysis of decision-making under conditions of extreme poverty, and risk. The author argues that publicly observable celebrations have two functions: they provide a space for maintaining social reputations, and webs of obligation, and, they serve as arenas for status-making competitions. The first role is central to maintaining the networks essential for social relationships, and coping with poverty. The second is a correlate of mobility that may become more prevalent as incomes rise. Development policies that favor individual over collective action, reduce the incentives for the networking function, and increase the incentives for status-enhancing functions - thus reducing social cohesion, and increasing conspicuous consumption. Market-driven improvements in urban employment, for example, could reduce a family's dependence on its traditional networks, could reduce incentives to maintain these networks, and could reduce social cohesion within a village, and thus its capacity for collective action. In contrast, micro-finance programs, and social funds try to retain, and even build a community's capacity for collective action.Health Monitoring&Evaluation,Environmental Economics&Policies,Anthropology,Education and Society,Health Economics&Finance

Modulating interaction times in an artificial society of robots

In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots' motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions

Collective Decision Dynamics in the Presence of External Drivers

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision-making. Our results indicate that 1) social networks lead to clustering and cohesive action among individuals, 2) binary information introduces high temporal variability and stagnation, and 3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure

Public Goods or Virtual Commons? Applying Theories of Public Goods, Social Dilemmas, and Collective Action to Electronic Networks of Practice

Electronic networks of practice are self-organizing, open activity systems focused on a shared practice that exist primarily through computer-mediated communication. These networks create a public good of knowledge that is available to anyone in the network, making it easy for individuals to free-ride on the efforts of others. Theories of collective action are reviewed to explain why individuals choose to actively participate in collective activities when the rational individual decision would be to free-ride on the efforts of others. These theories are applied to examine participation in electronic networks of practice, suggesting that participation in these networks is dependent upon 1) the attributes of the individuals in the collective, 2) the relational structure of social ties between individuals in the collective, 3) the norms of behavior of the collective, 4) the affective factors of the collective, and 5) the development of sanctions for noncompliance with network norms. This paper discusses how the ability of a network to leverage these factors to promote collective action is dependent upon the openness of the network, the extent to which the relationships in the collective are based on computer-mediated communication, and the extent to which the critical resources in the network are characterized by public or private goods

Trust-Based Techniques for Collective Intelligence in Social Search Systems.

A key-issue for the effectiveness of collaborative decision support systems is the problem of the trustworthiness of the entities involved in the process. Trust has been always used by humans as a form of collective intelligence to support effective decision making process. Computational trust models are becoming now a popular technique across many applications such as cloud computing, p2p networks, wikis, e-commerce sites, social network. The chapter provides an overview of the current landscape of computational models of trust and reputation, and it presents an experimental study case in the domain of social search, where we show how trust techniques can be applied to enhance the quality of social search engine predictions