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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.

In this chapter, we consider the model of call center incoming call forecasting and staffing-level optimization. We first present the structure of the model and how an agent-based modeling technique could enrich the decision rule and the model. A matrix layout is introduced to present the model so that it can be understood in an efficient way from the perspective of a programmer. The agent-based queuing model will be used in forecasting. We then utilize the bisection method and stepwise method to optimize the staff level to satisfy a target range service-level criteria. Call center management could use the model in practice for their management forecasting and optimization decision-making process in terms of how many agents they need to achieve the target business efficiency goal.

In this chapter we examine some possibilities of using computer simulation methods to model the interaction of affect and cognition in organizations, with a particular focus on agent-based modeling (ABM) techniques. Our chapter has two main aims. First, we take stock of methodological progress in this area, highlighting important developments in the modeling of affect and cognition in other fields, including psychology and economics. Second, we outline how ABM in particular can help to advance managerial and organizational cognition by building and testing theoretical models predicated on the interaction of affect and cognition. We argue that using ABM for this purpose can improve the level of specificity of cognitive and affective concepts and their interrelationships in organizational theories, yield more behaviorally plausible models of behavior in and of organizations, and deepen understanding of the generative behavioral mechanisms of multi-level organizational phenomena. We highlight possibilities for using ABM to model affect–cognition interactions in studies of mental models, collective cognition, diversity in work groups and teams, and organizational decision-making.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

Organizational researchers live in two worlds. The first demands and rewards speculations about how to improve performance. The second demands and rewards adherence to rigorous standards of scholarship (March & Sutton, 1997, p. 698).Those of us who study organizations and are professors of management work on the front lines, so to speak, where the beliefs we have about how to improve managerial performance get passed directly on to practitioners. The question is, What right do we have to put our beliefs in a privileged position? Beliefs, by definition, are supposed to be true. According to Webster’s (1996) a belief is a conviction about the truth of some statement and/or reality of some phenomenon, especially when based on examination of evidence. Are all of our lectures based on consensually agreed upon evidentiary standards? What are these standards and who should maintain them?

The clear specification of leadership efforts spanning levels of analysis has lagged behind leadership research in general. Simulation modeling, such as agent-based modeling, provides research platforms for exploring these interesting issues. This chapter uses agent-based models, along with Dionne and Dionne's (2009) choices of leadership styles, to examine the impact of those styles on the generation of an emergent group resource, context-for-learning (CFL), instead of the specific task outcome (group decision making) described by Dionne and Dionne. Consistent effectiveness is found across leadership styles for workgroups with high and slightly lower initial individual levels of a CFL. A second agent-based model includes the ability of agents to forget previous learned skills and reveals a reduced effectiveness of all leadership styles. However, the effectiveness of the leadership styles differs between the two outcomes (the specific group task model and the emergent group resource model). Reasons for these differences are explored, and implications from the comparisons of the two models are delineated.

This chapter wants to understand under which circumstances and conditions non-traditional aids are effective in the strategic process. This study builds an agent-based computational simulation model – the S-uFUNK 2.1.0 – to explore the research question. The model features a group of managers that seeks to interpret environmental cues using both traditional and non-traditional tools. When interpretations converge, the group then settles on different focus areas to define a business strategy for their organization. The process is set in a way such that 11 parameters can be manipulated to explore the different conditions under which non-traditional aids are of use. Results suggest that non-traditional aids differ from traditional aids only in limited circumstances and that social dynamics and dispositions within the group are crucial. In general, the simulation helps us reflect on the way in which we consider traditional aids to strategy. In fact, if they are no different than non-traditional aids, their effectiveness is directly challenged.

For various reasons consumers find some innovations undesirable. These are identified as resistant innovations or those innovations that consumers are unable or unwilling to readily embrace, such as screw caps on fine wines, hybrid automobiles and nanotechnology-based products. What makes these types of innovations undesirable to consumers? How should marketers introduce into the marketplace these types of innovations? These questions are the foundation of my ongoing research. In reflecting on my research decisions for the past ten years, I came upon my application for PhD programs. In 1997, I wrote:“The past ten years I have been focused on product development for telecom firms, including five years as an entrepreneur. As I've worked, questions have frequently surfaced about the slow acceptance in the marketplace of some telecommunication offerings; Why has ISDN (Integrated Services Digital Network) not taken off? Why did 30,000 perfectly functioning Newton PDAs (Personal Digital Assistant) end up at the Los Angeles dump? Did AT&T misjudge the market timing for videophones? What role, if any, did marketing play in these product mis-launches?”…I have explored disruptive technologies with Christensen (1997), crossed the chasm with Moore (1991), searched for the sources of innovation with Von Hippel (1988), and evaluated Roger's diffusion theories (1995) looking for direction. These investigations have led to more questions.