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1 – 10 of 323This chapter reconstructs the garbage can model (GCM) of organizational choice as an agent-based model. Subsequently, it modifies the original model by establishing behavioral…
Abstract
This chapter reconstructs the garbage can model (GCM) of organizational choice as an agent-based model. Subsequently, it modifies the original model by establishing behavioral rules that regulate processes of organizational founding, growth, and disbanding in an artificial garbage can ecology. This population-level GCM reproduces some of the core features of the original GCM. Furthermore, it produces aggregate regularities that are broadly consistent with the historical trajectories followed by actual organizational populations.
Julia V. Ragulina and Alexander A. Chursin
To address management issues in the development of flexible production systems in the enterprises of knowledge-intensive industries, this chapter considers four basic approaches…
Abstract
To address management issues in the development of flexible production systems in the enterprises of knowledge-intensive industries, this chapter considers four basic approaches to planning production processes. Based on these approaches, the methodology of the agent-based approach, which satisfies the fundamental requirements of today's production systems, is formulated, with much attention paid to the rules of dispatching as a key tool of operational control over the production plan and its implementation. The advantage of simulation-based approaches is that they can dynamically adjust the ongoing integration of planning, depending on the state of flexible production systems, in the use of combined approaches and methods of management of production processes.
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Jessica L. Wildman and Eduardo Salas
There has been a lack of focus on multi-level issues within leadership research. Dionne and Dionne (2009) address this gap in the research by presenting a Monte Carlo simulation…
Abstract
There has been a lack of focus on multi-level issues within leadership research. Dionne and Dionne (2009) address this gap in the research by presenting a Monte Carlo simulation examining leadership at four levels of analysis within a group decision-making context. While their work makes a strong contribution to the sciences of leadership, group decision making, and team complexity, many aspects of the research demonstrate potential for great expansion and improvement. Toward this purpose, this commentary discusses and provides suggestions regarding the topics of computer simulation in team research, group decision-making theory, and the modeling of team complexity. It is intended to stimulate continued critical thinking and more innovative, practical, and carefully designed research efforts.
Peter Docherty, Mari Kira and Abraham B. (Rami) Shani
A work system may be said to exhibit social sustainability if it utilizes its human, social, economic, and ecological resources with responsibility. This entails using these…
Abstract
A work system may be said to exhibit social sustainability if it utilizes its human, social, economic, and ecological resources with responsibility. This entails using these resources in a non-exploitive way, regenerating them, and paying due attention to the needs and ambitions of its stakeholders in the short- and long-term. For most presently existing organizations attaining and maintaining sustainability requires a midcourse correction, a transformation process. This chapter reviews the main concepts regarding sustainability and previous research of organizational development in this context. It presents a four-phase model for this transformation process and illustrates the model's application in four different contexts. The results are discussed and directions for further research are presented.
Stanislav Ivanov and Craig Webster
Purpose: The purpose of this chapter is to elaborate on the major conceptual and practical considerations of the use of robots, artificial intelligence and service automation…
Abstract
Purpose: The purpose of this chapter is to elaborate on the major conceptual and practical considerations of the use of robots, artificial intelligence and service automation (RAISA) in travel, tourism, and hospitality companies (TTH).
Design/methodology/approach: The chapter develops a conceptual framework of the major issues related to the use of RAISA in the travel, tourism and hospitality context.
Findings: The findings indicate that while there is a creeping incursion of RAISA into TTH, there are major concerns that the TTH industry has to consider in regard to automating TTH services.
Practical implications: In a practical sense, the chapter identifies the decisions that TTH industry professionals need to take when dealing with RAISA technologies. Furthermore, the chapter elaborates on the impacts RAISA have on business operations, marketing management, human resources and financial management of TTH companies. The TTH industry has to adjust its practices and communicate with its workforce in ways as not to increase Luddite tendencies and resistance among employees.
Social implications: The analysis shows that there is an upcoming era in which automation of services will be so advanced that wealthy countries may not need to import labour to make up with its own aging workforce, suggesting that RAISA and its further development has the potential for disrupting society and international relations.
Originality/value: This chapter provides a comprehensive review of the issues related to the use of RAISA in the TTH industry, including the drivers of RAISA adoption in tourism, advantages and disadvantages of RAISA technologies compared to human employees, decisions that managers need to take, and the impacts of RAISA on business processes. It shows how macroenvironmental pressures shape the microeconomic decisions to use RAISA in a TTH context.
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Francesco Moscone, Veronica Vinciotti and Elisa Tosetti
This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and…
Abstract
This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.
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Chenfeng Xiong, Xiqun Chen and Lei Zhang
This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.
Abstract
Purpose
This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.
Theory
A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.
Findings
The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.
Originality and value
Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.
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Mohammad Raoufi, Nima Gerami Seresht, Nasir Bedewi Siraj and Aminah Robinson Fayek
Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex…
Abstract
Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.
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Sharon Purchase, Sara Denize and Doina Olaru
This chapter outlines a method for developing simulation code from case-based data using narrative sequence analysis. This analytical method allows researchers to systematically…
Abstract
This chapter outlines a method for developing simulation code from case-based data using narrative sequence analysis. This analytical method allows researchers to systematically specify the ‘real-world’ behaviours and causal mechanisms that describe the research problem and translate this mechanism into simulation code. An illustrative example of the process used for code development from case-based data is detailed using a well-documented case of photovoltaic innovation. Narrative sequence analysis is used to analyse case data. Micro-sequences are identified and simplified. Each micro-sequence is presented first in pseudo-code and then in simulation code. This chapter demonstrates the coding process using Netlogo code. Narrative sequence analysis provides a rigorous and systematic approach to identifying the underlying mechanisms to be described when building simulation models. This analytical technique also provides necessary and sufficient information to write simulation code. This chapter addresses a current gap in the methodology literature by including case data within agent-based model building processes. It benefits B2B marketing researchers by outlining guiding processes and principles in the use of case-based data to build simulation models.
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