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11 articles matched your search for the keywords:
Organisational Learning, Experience-Based Learning, Exploration, Exploitation, Knowledge Management, Genetic Algorithms

Cricketsim: a Genetic and Evolutionary Computer Simulation

Kyle Wagner and Kerry Shaw
Journal of Artificial Societies and Social Simulation 11 (1) 3

Kyeywords: Individual-Based Model, Genetic Algorithms, Communication, Sexual Signaling, Speciation, Evolution, Genetics
Abstract: We present cricketsim, an individual-based simulator of species and community dynamics that allows experimenters to manipulate genetic and evolutionary parameters as well as parameters affecting the simulated environment and its inhabitants. The simulator can model genotypic and phenotypic features of species, such as male signals and female preferences, as well as demographic and fitness-related features. The individual-based simulator creates a lattice (cellular) world in which males and females interact by moving, signaling/responding, and mating. One or more species evolves over simulation time as individuals of a species interact with others during its lifetime, possibly creating new offspring through successful mating. The program\'s design, parameters, execution and data collection are described, an example experiment is presented, and several applications are discussed.

Network Formation and Strategic Firm Behaviour to Explore and Exploit

Müge Özman
Journal of Artificial Societies and Social Simulation 11 (1) 7

Kyeywords: Networks, Knowledge, Innovation, Exploitation, Exploration
Abstract: The aim of this paper is to investigate the effect of technological opportunities and knowledge tacitness on inter-firm network formation, under two different industry regimes. In the first regime environment is stable and the aim of firms is to exploit knowledge. In this case, they attribute more value to repeated interactions with geographically close firms. In the second regime, there is environmental turbulence, which increases the value of access to novelties from distant partners for the purpose of exploration. The question addressed is, under these regimes how do technological opportunities and knowledge tacitness influence structure of networks? A simulation model is carried out where firms select partners and learn from them, which further shapes their selection process. How the macro structure of the network is shaped from the individual partner selection decisions of firms is analysed. The results reveal that in both regimes richer technological opportunities and higher tacitness generates local and global star firms depending on the parameter range.

Agent-Based Models and Simulations in Economics and Social Sciences: From Conceptual Exploration to Distinct Ways of Experimenting

Denis Phan and Franck Varenne
Journal of Artificial Societies and Social Simulation 13 (1) 5

Kyeywords: Agent-Based Models and Simulations, Epistemology, Economics, Social Sciences, Conceptual Exploration, Model World, Credible World, Experiment, Denotational Hierarchy
Abstract: Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility.

Why Bother with What Others Tell You? An Experimental Data-Driven Agent-Based Model

Riccardo Boero, Giangiacomo Bravo, Marco Castellani and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 13 (3) 6

Kyeywords: Reputation, Trustworthiness, Laboratory Experiment, Agent-Based Model, Exploration Vs. Exploitation
Abstract: This paper investigates the relevance of reputation to improve the explorative capabilities of agents in uncertain environments. We have presented a laboratory experiment where sixty-four subjects were asked to take iterated economic investment decisions. An agent-based model based on their behavioural patterns replicated the experiment exactly. Exploring this experimentally grounded model, we studied the effects of various reputational mechanisms on explorative capabilities at a systemic level. The results showed that reputation mechanisms increase the agents\' capability for coping with uncertain environments more than individualistic atomistic exploration strategies, although the former does entail a certain amount of false information inside the system.

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

Jennifer Kunz
Journal of Artificial Societies and Social Simulation 14 (4) 18

Kyeywords: Organisational Learning, Experience-Based Learning, Exploration, Exploitation, Knowledge Management, Genetic Algorithms
Abstract: 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.

An Agent-Based Social Network Model of Binge Drinking Among Dutch Adults

Philippe Giabbanelli and Rik Crutzen
Journal of Artificial Societies and Social Simulation 16 (2) 10

Kyeywords: Conceptual Exploration, Drinking Motives, Social Influence
Abstract: Binge drinking is a complex social problem linked to an array of detrimental health effects. While binge drinking in youth has been analyzed extensively using traditional methods (e.g., regressions analyses), the adult population has received less attention, and recent work has exemplified the potential for simulations to help scholars and practitioners better understand the problem. In this paper, we used agent-based social network models to test a number of hypotheses on important aspects of binge drinking in a sample representative of the adult Dutch population. In particular, we found that a combination of simple social rules (choosing peers who are similar, being prompted to drink if at least a fraction of them drinks, and incorporating the context) was sufficient to correctly predict the behaviour of half of the binge drinkers and 4 out of 5 non binge drinkers. Furthermore, we used factorial analyses to examine the contribution and combination of hypotheses in predicting the behaviour of individuals, with results indicating that who we interact with may not matter so much as how we interact. Finally, we evaluated the potential for interventions that mediate interactions between people in order to reduce the prevalence of binge drinking and found that the impact of such interventions was non linear: moderate interventions would yield benefits, but stronger interventions may only be of limited further benefit.

Individual Bias and Organizational Objectivity: An Agent-Based Simulation

Bo Xu, Renjing Liu and Weijiao Liu
Journal of Artificial Societies and Social Simulation 17 (2) 2

Kyeywords: Individual Bias, Agent-Based Modeling, Diversity, Exploration, Exploitation
Abstract: We introduce individual bias to the simulation model of exploration and exploitation and examine the joint effects of individual bias and other parameters, aiming to answer two questions: First, whether reducing individual bias can increase organizational objectivity? Second, whether measures, such as increasing organization size, can increase organizational objectivity in the presence of individual bias? Our results show that individual bias has both positive and negative effects, and reducing individual bias may be not beneficial when organization size is large or environment is turbulent. Diverse knowledge resulting from large organization size can help avoid the negative effects of individual bias when the bias is strong enough so that the individuals who are less limited by bias can be distinguished as the source of learning. Our results also suggest that increasing interpersonal learning, decreasing learning from the organization, task complexity, and environmental turbulence, and maintaining personnel turnover can improve organizational objectivity in the presence of individual bias.

Optimization of Agent-Based Models: Scaling Methods and Heuristic Algorithms

Matthew Oremland and Reinhard Laubenbacher
Journal of Artificial Societies and Social Simulation 17 (2) 6

Kyeywords: Agent-Based Modeling, Optimization, Statistical Test, Genetic Algorithms, Reduction
Abstract: Questions concerning how one can influence an agent-based model in order to best achieve some specific goal are optimization problems. In many models, the number of possible control inputs is too large to be enumerated by computers; hence methods must be developed in order to find solutions that do not require a search of the entire solution space. Model reduction techniques are introduced and a statistical measure for model similarity is proposed. Heuristic methods can be effective in solving multi-objective optimization problems. A framework for model reduction and heuristic optimization is applied to two representative models, indicating its applicability to a wide range of agent-based models. Results from data analysis, model reduction, and algorithm performance are assessed.

Entrepreneurial Team Learning, Forgetting and Knowledge Levels in Business Incubators: An Exploration and Exploitation Perspective

Wenqing Wu, Saixiang Ma, Kai Wang, Sang-Bing Tsai and Wen-Pin Lin
Journal of Artificial Societies and Social Simulation 22 (1) 10

Kyeywords: Entrepreneurial Team, Exploration·Exploitation, Business Incubator, Environmental Changes, Forgetting
Abstract: Exploration and exploitation are common in entrepreneurial teams. This paper considers the relationship among entrepreneurial teams in business incubators (BIETs) and the relationship between leaders and members of BIETs. It also examines the effects of BIET learning, forgetting and exit and entry on their knowledge level (KL) in different environments and models; two general situations involving the development and use of knowledge in BIETs and business incubators. The results indicate that in a static environment, the rate of BIET learning from each other and BIET equilibrium KL are negatively correlated, but a moderate rate of forgetting leads to a positive correlation. Second, in a static environment within a BIET, the combination of the leader learning from members quickly and members learning from the leader slowly can improve BIETs’ KL. However, with forgetting, improving BIETs’ KL requires a combination of fast learning by the leader and moderate learning by members. Third, in a dynamic environment, maintaining a moderate amount of exit and entry and forgetting within BIETs moderately improves BIETs’ KL in the long run. This effect is even more significant with BIETs’ exit and entry.

How to Manage Individual Forgetting: Analysis and Comparison of Different Knowledge Management Strategies

Jie Yan, Renjing Liu, Zhengwen He and Xiaobo Wan
Journal of Artificial Societies and Social Simulation 22 (4) 2

Kyeywords: Forgetting, Knowledge Management Strategy, Exploration-Exploitation, Agent-Based Modeling
Abstract: The creation, transfer and retention of knowledge in an organization has always been the focus of knowledge management researchers; however, one aspect of the dynamics of knowledge, i.e., forgetting, has received comparatively limited attention. To fill this research gap, we extend the basic simulation model proposed by March by incorporating forgetting and three knowledge management strategies, i.e., personalization, codification, and mixed, to explore the impacts of different knowledge management strategies and forgetting on the organizational knowledge level. The simulation results not only clarify the specific measures used to manage individual forgetting in each knowledge management strategy but also identify the boundary conditions under which knowledge management strategies should be adopted under different conditions.

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

Chaitanya Kaligotla, Jonathan Ozik, Nicholson Collier, Charles M. Macal, Kelly Boyd, Jennifer Makelarski, Elbert S. Huang and Stacy T. Lindau
Journal of Artificial Societies and Social Simulation 23 (4) 1

Kyeywords: Agent-Based Modeling, Model Exploration, High-Performance Computing, Active Learning
Abstract: This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.