Special Issue "Computational Organization Theory"

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A special issue of Administrative Sciences (ISSN 2076-3387).

Deadline for manuscript submissions: closed (1 September 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Myong-Hun Chang

Department of Economics, Cleveland State University, Cleveland, OH 44115, USA
Website | E-Mail
Interests: computational economics; computational organization theory; industrial organization; social networks; game theory

Special Issue Information

Dear Colleagues,

Computational Organization Theory is an emerging interdisciplinary area of research that applies the computational modeling and analysis technique to the questions that lie at the intersection of fields such as sociology, economics, political science, organizational science, and many others. The perspective that is common among all approaches within this area is that an organization is a system of many autonomous agents interacting with one another through a set of rules that are specific to the organization. The “organization” can be as small as a team or a group of a few individuals or as large as the entire social system that contains smaller organizations of varying sizes.
The agents are typically assumed to be purposive – i.e., have well-defined objectives that they try to meet, albeit imperfectly – but are less than fully rational in the sense of having limited foresight and imperfect computational capability. While autonomous in decision making, the agents are nevertheless social and engage in interactive relationships with each other, through either formal organizational mechanism that are imposed on them from top down or an informal system of beliefs and understandings that arise endogenously from the bottom up. Computational Organization Theory offers an approach that allows us to rigorously study the evolving nature of these formal and informal organizational relationships through a series of computational experiments.
This special issue seeks contributions using computational models to address a broad set of issues that arise in the conceptual framework described above. These issues include, but are not limited to the following:

  • Organizational learning when the organization consists of autonomous agents capable of learning individually
  • Exploration vs. exploitation (or innovation vs. imitation) at the level of agents within an organization and their impact on the organization’s performance over time
  • Impact of an improvement in information technology on the optimal structure of an organization (both formal and informal)
  • Simplicity and complexity of an organizational problem and their impact on the structure of the organizational hierarchy dedicated to problem-solving
  • Endogenous structure of a learning network within a social and/or an organizational system
  • Endogenous division of cognitive labor within a social and/or an organizational system
  • Organizational structure and the organization’s capability to adapt in a turbulent environment
  • Strategy-and-structure nexus

Prof. Dr. Myong-Hun Chang
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Administrative Sciences is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • computational model
  • organizational learning
  • organizational search
  • organizational structure
  • complexity
  • exploration
  • exploitation
  • social network
  • adaptive organization

Published Papers (4 papers)

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Research

Open AccessArticle Vaccination Games with Peer Effects in a Heterogeneous Hospital Worker Population
Adm. Sci. 2015, 5(1), 2-26; doi:10.3390/admsci5010002
Received: 1 September 2014 / Accepted: 16 December 2014 / Published: 14 January 2015
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Abstract
We develop a game theoretic model to analyze the Nash equilibrium of vaccine decisions in a hospital population with heterogeneous contacts. We use the model in conjunction with person-to-person contact data within a large university hospital. We simulate, using agent-based models, the probability
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We develop a game theoretic model to analyze the Nash equilibrium of vaccine decisions in a hospital population with heterogeneous contacts. We use the model in conjunction with person-to-person contact data within a large university hospital. We simulate, using agent-based models, the probability of infection for various worker types in the data and use these probabilities to identify the Nash equilibrium vaccine choices of hospital workers. The analysis suggests that there may be large differences in vaccination rates among hospital worker groups. We extend the model to include peer effects within the game. The peer effects may create additional equilibria or may further cement existing equilibria depending on parameter values. Further, depending on the magnitude of the peer effects and the costs of infection and vaccination, peer effects may increase or decrease differences in worker group vaccination rates within the hospital. Full article
(This article belongs to the Special Issue Computational Organization Theory)
Open AccessArticle Competition in a New Industrial Economy: Toward an Agent-Based Economic Model of Modularity
Adm. Sci. 2014, 4(3), 192-218; doi:10.3390/admsci4030192
Received: 28 February 2014 / Revised: 4 June 2014 / Accepted: 18 June 2014 / Published: 4 July 2014
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Abstract
When firms (conglomerates) are competing, not only for the present, with a given population of customers and a fixed set of commodities or service, but also for the future, in which products are constantly evolving, what will be their competitive strategies and what
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When firms (conglomerates) are competing, not only for the present, with a given population of customers and a fixed set of commodities or service, but also for the future, in which products are constantly evolving, what will be their competitive strategies and what will be the emerging ecology of the market? In this paper, we use the agent-based modeling of a modular economy to study the markup rate dynamics in a duopolistic setting. We find that there are multiple equilibria in the market, characterized by either a fixed point or a limit cycle. In the former case, both firms compete with the same markup rate, which is a situation similar to the familiar classic Bertrand model, except that the rate is not necessarily zero. In the latter case, both firms survive by maintaining different markup rates and different market shares. Full article
(This article belongs to the Special Issue Computational Organization Theory)
Open AccessArticle Individual Learning and Social Learning: Endogenous Division of Cognitive Labor in a Population of Co-evolving Problem-Solvers
Adm. Sci. 2013, 3(3), 53-75; doi:10.3390/admsci3030053
Received: 17 May 2013 / Revised: 28 June 2013 / Accepted: 2 July 2013 / Published: 12 July 2013
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Abstract
The dynamic choice between individual and social learning is explored for a population of autonomous agents whose objective is to find solutions to a stream of related problems. The probability that an agent is in the individual learning mode, as opposed to the
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The dynamic choice between individual and social learning is explored for a population of autonomous agents whose objective is to find solutions to a stream of related problems. The probability that an agent is in the individual learning mode, as opposed to the social learning mode, evolves over time through reinforcement learning. Furthermore, the communication network of an agent is also endogenous. Our main finding is that when agents are sufficiently effective at social learning, structure emerges in the form of specialization. Some agents focus on coming up with new ideas while the remainder of the population focuses on imitating worthwhile ideas. Full article
(This article belongs to the Special Issue Computational Organization Theory)
Open AccessArticle Autonomy, Conformity and Organizational Learning
Adm. Sci. 2013, 3(3), 32-52; doi:10.3390/admsci3030032
Received: 14 May 2013 / Revised: 24 June 2013 / Accepted: 28 June 2013 / Published: 5 July 2013
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Abstract
There is often said to be a tension between the two types of organizational learning activities, exploration and exploitation. The argument goes that the two activities are substitutes, competing for scarce resources when firms need different capabilities and management policies. We present another
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There is often said to be a tension between the two types of organizational learning activities, exploration and exploitation. The argument goes that the two activities are substitutes, competing for scarce resources when firms need different capabilities and management policies. We present another explanation, attributing the tension to the dynamic interactions among search, knowledge sharing, evaluation and alignment within organizations. Our results show that successful organizations tend to bifurcate into two types: those that always promote individual initiatives and build organizational strengths on individual learning and those good at assimilating the individual knowledge base and exploiting shared knowledge. Straddling the two types often fails. The intuition is that an equal mixture of individual search and assimilation slows down individual learning, while at the same time making it difficult to update organizational knowledge because individuals’ knowledge base is not sufficiently homogenized. Straddling is especially inefficient when the operation is sufficiently complex or when the business environment is sufficiently turbulent. Full article
(This article belongs to the Special Issue Computational Organization Theory)

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