EMERGING THE EMERGENCE SOCIOLOGY: The Philosophical Framework of Agent-Based Social Studies

The structuration theory originally provided by Anthony Giddens and the advance improvement of the theory has been trying to solve the dilemma came up in the epistemological aspects of the social sciences and humanity. Social scientists apparently have to choose whether they are too sociological or too psychological. Nonetheless, in the works of the classical sociologist, Emile Durkheim, this thing has been stated long time ago. The usage of some models to construct the bottom-up theories has followed the vast of computational technology. This model is well known as the agent based modeling. This paper is giving a philosophical perspective of the agent-based social sciences, as the sociology to cope the emergent factors coming up in the sociological analysis. The framework is made by using the artificial neural network model to show how the emergent phenomena came from the complex system. Understanding the society has self-organizing (autopoietic) properties, the Kohonen’s self-organizing map is used in the paper. By the simulation examples, it can be seen obviously that the emergent phenomena in social system are seen by the sociologist apart from the qualitative framework on the atomistic sociology. In the end of the paper, it is clear that the emergence sociology is needed for sharpening the sociological analysis in the emergence sociology

Simulating tourists' behaviour using multi-agent modelling

We discuss who should be in charge of providing data relevant to marketing segmentation for the tourism industry. We describe the difficulties of using the most commonly found consumer behavioural models within an information system, and oppose them to a novel approach in marketing segmentation, based on outgoings analysis. We use agent-modelling techniques, based on cellular automaton rules and stochastic processes to implement our model and generate sales data. We then present our algorithm to identify similarly behaved tourists, showing that the commonly used “nationality” variable for segments discrimination is not efficient. We conclude with some test runs results discussion and possible further research tracks.Simulation; Stochastic processes; Cellular automata; Tourism; Business; Public Policy Issues; Management techniques; Marketing; Market segmentation; Customer behaviour model

Real Options and Competition: The Impact of Depreciation and Reinvestment

International Relations/Trade,

The separation of economic versus EA parameters in EA-learning.

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. Current economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters. In this paper we show that this practice may hinder the performance of the GA and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing two widely used approaches to population learning with respect to their convergence properties and robustnes

Does vertical integration reduce investment reluctance in production chains? An agent-based real options approach

This paper uses an agent-based real options approach to analyze whether stronger vertical integration reduces investment reluctance in pork production. A competitive model in which firms identify optimal investment strategies by using genetic algorithms is developed. Two production systems are compared: a perfectly integrated system and a system in which firms produce either the intermediate product (piglets) or the final product (pork). Simulations show that the spot market solution and the perfectly integrated system lead to a very similar production dynamics even with limited information on production capacities. The results suggest that, from a pure real options perspective, spot markets are not significantly inferior to perfectly integrated supply chains.real options, supply chain, agent-based models, genetic algorithms, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Institutional and Behavioral Economics, Productivity Analysis,

Group-Level Exploration and Exploitation: A Computer Simulation-Based Analysis

Organisational research has studied the tension between exploration and exploitation for years. In essence, this body of research agrees on the necessity of a balance between explora-tive and exploitative processes to prevent an organisation from falling into a learning trap. Thus, to enhance the active management of this balance in organisations, a deeper theoretical understanding of the factors that influence the development of exploration and exploitation has to be gained. One of the recently discussed factors is the interplay between exploration and exploitation on different organisational levels. This paper picks up this discussion. It pro-vides an in-depth, computer simulation-based analysis of the performance of organisational types with varying degrees of within-group and between-group exploration and exploitation in situations of different degrees of task complexity. The findings indicate that a high share of between-group processes as compared to within-group processes positively influences the organisational performance level and that dependent on task complexity the optimal share of exploration and exploitation varies.Organisational Learning, Experience-Based Learning, Exploration, Exploitation, Knowledge Management, Genetic Algorithms

Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory mid-point pricing. Buyers and sellers use Roth-Erev individual reinforcement learning to determine their price and quantity offers in each auction round. It is shown that market microstructure is strongly predictive for the relative market power of buyers and sellers, and that high market efficiency is generally attained. These findings are robust for tested changes in individual learning parameters. It is also shown that similar relative market power findings are obtained if the electricity buyer and seller populations instead each engage in social mimicry learning via a genetic algorithm. However, market efficiency is substantially reduced.Wholesale electricity market, Electricity restructuring, Double auction, Market power, Efficiency, Concentration, Capacity, Agent-based computational economics, Roth-Erev reinforcement learning, Genetic algorithm social learning.