Knowledge-Based Externalities and Geographical Clusters

Agglomeration economies are positive externalities associated with the co-location of firms within a bounded geographic area. Traditionally, these agglomerative advantages have been expressed in terms of pecuniary externalities and they have been identified as one of the key sources of geographical cluster (GC) competitive advantage. However, in the last years the basics of competition are changed and the ability of firms to create new knowledge is more crucial for success rather than the efficiency in production. This has shifted the attention of scholars on the role of knowledge and learning in GCs. In line with these studies, this chapter suggests that agglomeration economies are related to both pecuniary externalities and knowledge-based externalities. The latter are benefits that co-located firms can gain in terms of development of knowledge. To investigate whether knowledge-based externalities affect geographical clustering of firms, an agent-based model is developed. By using this model, a simulation analysis is carried out.

Simulating an Incentive Framework for Scientific Production by Means of Adaptive Agents

This chapter describes an agent-based simulation of an incentive mechanism for scientific production. In the proposed framework, a central agency is responsible for devising and enforcing a policy consisting of performance-based incentives in an attempt to induce a global positive behavior of a group of researchers, in terms of number and type of scientific publications. The macro-level incentive mechanism triggers micro-level actions that, once intensified by social interactions, lead to certain patterns of behavior from individual agents (researchers). Positive reinforcement from receiving incentives (as well as negative reinforcement from not receiving them) shape the behavior of agents in the course of the simulation. The authors show, by means of computational experiments, that a policy devised to act at the individual level might induce a single global behavior that can, depending on the values of certain parameters, be distinct from the original target and have an overall negative effect. The agent-based simulation provides an objective way of assessing the quantitative effect that different policies might induce on the behavior of individual researchers when it comes to their preferences regarding scientific publications.

Social Space in Simulation Models

In real world scenarios, the formation of consensus is a self-organisation process by which actors have to make a joint assessment about a target subject, be it a decision making problem or the formation of a collective opinion. In social simulation, models of opinion dynamics tackle the opinion formation phenomena. These models try to make an assessment, for instance, of the ideal conditions that lead an interacting group of agents to opinion consensus, polarisation or fragmentation. This chapter investigates the role of social relation structure in opinion dynamics and consensus formation. The authors present an agent-based model that defines social relations as multiple concomitant social networks and explore multiple interaction games in this structural set-up. They discuss the influence of complex social network topologies where actors interact in multiple distinct networks. The chapter builds on previous work about social space design with multiple social relations to determine the influence of such complex social structures in a process such as opinion formation.

On Agent Interactions Governed by Morality

Morality tells agents what they ought to do, and this defines their identity and character. This chapter deals with moral behaviour, following the classical view in Philosophy that defends character as a state concerned with choice, and able to direct the agent decision-taking. The authors also include new values regarding agent moral signature that may enhance the evaluation of agents, namely on reputation and satisfaction. So, the popularity of the agents can be measured with more depth, and not only for organizations but also for social networks.

Building ABMs to Control the Emergence of Crisis Analyzing Agents' Behavior

Agent-based models (ABMs) are quite new in the modeling landscape; they emerged on the scene in the 1990s. ABMs have a clear advantage over other approaches: they create the capacity to manage learning processes in agents and discover novelties in their behavior. In addition to bounded rationality assumptions, ABMs share a number of peculiar characteristics: first of all, a bottom-up perspective is assumed where the properties of macro-dynamics are emergent properties of micro-dynamics involving individuals as heterogeneous agents who live in complex systems that evolve through time. To apply this framework to financial crisis analysis, a simplified implementation of the SWARM protocol (www.swarm.org), based on Python, is introduced. The result is the Swarm-Like Agent Protocol in Python (SLAPP). Using SLAPP, it is possible to focus on natural phenomena and social behavior. In the case of this chapter, the authors focus on the banking system, recreating the interactions of a community of financial institutions that act in the payment system and in the interbank market for short-term liquidity.

Agent-Based Simulation of Electric Energy Consumers

The electric energy consumption is one of the main indicators of both the economic development and the quality of life of a society. However, the electric energy consumption data of individual home use is hard to obtain due to several reasons, such as privacy issues. In this sense, the social simulation based on multiagent systems comes as a promising option to deal with this difficulty through the production of synthetic electric energy consumption data. In a multiagent system the intelligent global behavior can be achieved from the behavior of the individual agents and their interactions. This chapter proposes a tool for simulation of electric energy consumers, based on multiagent systems concepts using the NetLogo tool. The tool simulates the residential consumption during working days and presented as a result the synthetic data average monthly consumption of residences, which varies according to income. So, the analysis of the produced simulation results show that economic consumers of the income 1 in the summer season had the lowest consumption among all other consumers and consumers noneconomic income 6 in the winter season had the highest.

Large-Scale Social Simulation, Dealing with Complexity Challenges in High Performance Environments

Advances on information technology in the past decades have provided new tools to assist scientists in the study of social and natural phenomena. Agent-based modeling techniques have flourished recently, encouraging the introduction of computer simulations to examine behavioral patterns in complex human and biological systems. Real-world social dynamics are very complex, containing billions of interacting individuals and an important amount of data (both spatial and social). Dealing with large-scale agent-based models is not an easy task and encounters several challenges. The design of strategies to overcome these challenges represents an opportunity for high performance parallel and distributed implementation. This chapter examines the most relevant aspects to deal with large-scale agent-based simulations in social sciences and revises the developments to confront technological issues.

Exploring Emergence within Social Systems with Agent Based Models

In this chapter, the authors examine manifestations of emergence or apparent emergence in agent based social modeling and simulation, and discuss the inherent challenges in building real world models and in defining, recognizing and validating emergence within these systems. The discussion is grounded in examples of research on emergence by others, with extensions from within our research group. The works cited and built upon are explicitly chosen as representative samples of agent-based models that involve social systems, where observation of emergent behavior is a sought-after outcome. The concept of the distinctiveness of social from abiotic emergence in terms of the use of global parameters by agents is introduced.

A Unified Framework for Traditional and Agent-Based Social Network Modeling

In the last sixty years of research, several models have been proposed to explain (i) the formation and (ii) the evolution of networks. However, because of the specialization required for the problems, most of the agent-based models are not general. On the other hand, many of the traditional network models focus on elementary interactions that are often part of several different processes. This phenomenon is especially evident in the field of models for social networks. Therefore, this chapter presents a unified conceptual framework to express both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model that acts as a template for other models. To support this meta-model, the chapter proposes a different kind of agent-based modeling tool that we specifically created for developing social network models. The tool the authors propose does not aim at being a general-purpose agent-based modeling tool, thus remaining a relatively simple software system, while it is extensible where it really matters. Eventually, the authors apply this toolkit to a novel problem coming from the domain of P2P social networking platforms.

Multi-Agent Economically Motivated Decision-Making

The key motivation for this chapter is the perception that within the near future, markets will be composed of individuals that may simultaneously undertake the roles of consumers, producers and traders. Those individuals are economically motivated “prosumer” (producer-consumer) agents that not only consume, but can also produce, store and trade assets. This chapter describes the most relevant aspects of a simulation tool that provides (human and virtual) prosumer agents an interactive and real-time game-like environment where they can explore (long-term and short-term) strategic behaviour and experience the effects of social influence in their decision-making processes. The game-like environment is focused on the simulation of electricity markets, it is named ITEM-game (“Investment and Trading in Electricity Markets”), and it is publically available (ITEM-Game, 2013) for any player to explore the role of a prosumer agent.