How groups can foster consensus: The case of local cultures

A local culture denotes a commonly shared behaviour within a cluster of firms. Similar to social norms or conventions, it is an emergent feature resulting from the firms' interaction in an economic network. To model these dynamics, we consider a distributed agent population, representing e.g. firms or individuals. Further, we build on a continuous opinion dynamics model with bounded confidence ($\epsilon$), which assumes that two agents only interact if differences in their behaviour are less than $\epsilon$. Interaction results in more similarity of behaviour, i.e. convergence towards a common mean. This framework is extended by two major concepts: (i) The agent's in-group consisting of acquainted interaction partners is explicitly taken into account. This leads to an effective agent behaviour reflecting that agents try to continue to interact with past partners and thus to keep sufficiently close to them. (ii) The in-group network structure changes over time, as agents can form new links to other agents with sufficiently close effective behaviour or delete links to agents no longer close in behaviour. Thus, our model provides a feedback mechanism between the agents' behaviour and their in-group structure. Studying its consequences by means of agent-based computer simulations, we find that for narrow-minded agents (low $\epsilon$) the additional feedback helps to find consensus more often, whereas for open-minded agents (high $\epsilon$) this does not hold. This counterintuitive result is explained by simulations of the network evolution

Evolution of Coordination in Social Networks: A Numerical Study

Coordination games are important to explain efficient and desirable social behavior. Here we study these games by extensive numerical simulation on networked social structures using an evolutionary approach. We show that local network effects may promote selection of efficient equilibria in both pure and general coordination games and may explain social polarization. These results are put into perspective with respect to known theoretical results. The main insight we obtain is that clustering, and especially community structure in social networks has a positive role in promoting socially efficient outcomes.Comment: preprint submitted to IJMP

A Unified Framework for Multi-Agent Agreement

Multi-Agent Agreement problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from distributed sensor networks, to meeting scheduling, to development of norms, conventions, and language. While much work has been done on particular agreement problems, no unifying framework exists for comparing MAPs that vary in, e.g., strategy space complexity, inter-agent accessibility, and solution type, and understanding their relative complexities. We present such a unification, the Distributed Optimal Agreement Framework, and show how it captures a wide variety of agreement problems. To demonstrate DOA and its power, we apply it to two well-known MAPs: convention evolution and language convergence. We demonstrate the insights DOA provides toward improving known approaches to these problems. Using a careful comparative analysis of a range of MAPs and solution approaches via the DOA framework, we identify a single critical differentiating factor: how accurately an agent can discern other agent.s states. To demonstrate how variance in this factor influences solution tractability and complexity we show its effect on the convergence time and quality of Particle Swarm Optimization approach to a generalized MAP

TOPOLOGY-AWARE APPROACH FOR THE EMERGENCE OF SOCIAL NORMS IN MULTIAGENT SYSTEMS

Social norms facilitate agent coordination and conflict resolution without explicit communication. Norms generally involve restrictions on a set of actions or behaviors of agents to a particular strategy and can significantly reduce the cost of coordination. There has been recent progress in multiagent systems (MAS) research to develop a deep understanding of the social norm formation process. This includes developing mechanisms to create social norms in an effective and efficient manner. The hypoth- esis of this dissertation is that equipping agents in networked MAS with “network thinking” capabilities and using this contextual knowledge to form social norms in an effective and efficient manner improves the performance of the MAS. This disser- tation investigates the social norm emergence problem in conventional norms (where there is no conflict between individual and collective interests) and essential norms (where agents need to explicitly cooperate to achieve socially-efficient behavior) from a game-theoretic perspective. First, a comprehensive investigation of the social norm formation problem is performed in various types of networked MAS with an emphasis on the effect of the topological structures on the process. Based on the insights gained from these network-theoretic investigations, novel topology-aware decentralized mech- anisms are developed that facilitate the emergence of social norms suitable for various environments. It addresses the convention emergence problem in both small and large conventional norm spaces and equip agents to predict the topological structure to use the suitable convention mechanisms. It addresses the cooperation emergence prob- lem in the essential norm space by harnessing agent commitments and altruism where appropriate. Extensive simulation based experimentation has been conducted on dif- ferent network topologies by varying the topological features and agent interaction models. Comparisons with state-of-the-art norm formation techniques show that pro- posed mechanisms facilitate significant improvement in performance in a variety of networks

Different reactions to adverse neighborhoods in games of cooperation

In social dilemmas, cooperation among randomly interacting individuals is often difficult to achieve. The situation changes if interactions take place in a network where the network structure jointly evolves with the behavioral strategies of the interacting individuals. In particular, cooperation can be stabilized if individuals tend to cut interaction links when facing adverse neighborhoods. Here we consider two different types of reaction to adverse neighborhoods, and all possible mixtures between these reactions. When faced with a gloomy outlook, players can either choose to cut and rewire some of their links to other individuals, or they can migrate to another location and establish new links in the new local neighborhood. We find that in general local rewiring is more favorable for the evolution of cooperation than emigration from adverse neighborhoods. Rewiring helps to maintain the diversity in the degree distribution of players and favors the spontaneous emergence of cooperative clusters. Both properties are known to favor the evolution of cooperation on networks. Interestingly, a mixture of migration and rewiring is even more favorable for the evolution of cooperation than rewiring on its own. While most models only consider a single type of reaction to adverse neighborhoods, the coexistence of several such reactions may actually be an optimal setting for the evolution of cooperation.Comment: 12 pages, 5 figures; accepted for publication in PLoS ON

MULTISCALAR CLUSTERS AND NETWORKS AS THE FOUNDATIONS OF INNOVATION DYNAMICS IN THE BIOPHARMACEUTICAL INDUSTRY

Based on the case of the biopharmaceutical industry, the aim of this paper is to challenge the core conviction now widespread within the “spatial clustering theory”, which devotes a key (if not exclusive) role to geographical proximity in explaining clustering dynamics of innovation activities within spe-cific territories. Our argument is threefold. First, mere geographical proximity is not enough; in many cases, cognitive, organizational and strategic forms of proximity are often at least as crucial as the topological closeness among inno-vation actors. Second, our idea is that clusters are fundamentally the territoria-lized outcome of combinations of inter-organizational and social networks among actors pursuing common goals, each of these actors having a specific territorial and social embedding that allows him or her (or not) to operate and interact at different spatial scales. These networks are socially and territorially embedded, but they can operate at various spatial scales. Third, sector-driven dynamics – as is in the case of biopharmaceuticals – may structurally frame the way the actors interact and collaborate in R&D projects and innovation proce-sses. Indeed, the dynamics underlying the emergence, structuring and evolution of biopharmaceutical clusters are both multi-actor and multiscalar. In this perspective, clusters and networks appear to be intertwined phenomena, con-substantial one to each other, and co-evolving organizational modes of biop-harmaceutical innovation.BIOCLUSTERS, FORMS OF PROXIMITY, INNOVATION NETWORKS, SPATIAL SCALES

Spatial Dispersion of Peering Clusters in the European Internet

We study the role played by geographical distance in the peering decisions between Internet Service Providers. Firstly, we assess whether or not the Internet industry shows clustering in peering; we then concentrate on the dynamics of the agglomeration process by studying the effects of bilateral distance in changing the morphology of existing peering patterns. Our results show a dominance of random spatial patterns in peering agreements. The sign of the effect of distance on the peering decision, driving the agglomeration/dispersion process, depends, however, on the initial level of clustering. We show that clustered patterns will disperse in the long run