The Development of an Agent-Based Modeling Framework for Simulating Engineering Team Work

Crowder, Richard; Robinson, Mark A.; Hughes, Helen; Sim, Yee Wai

doi:10.1109/tsmca.2012.2199304

Cited by 78 publications

(82 citation statements)

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“…All of these proposals will also require the refinement of our existing toolsets and the development of new methods, so that we are better placed to analyze, understand, predict and evaluate complex socio-technical systems (e.g., Crowder, Robinson, Hughes, & Sim, 2012;Walker, et al, 2010). We will need innovations in our toolsets, for example, including innovations such as simulation and modeling, social network analyzes, the use of 'big data' and the like.…”

Section: An Agenda For Research and Developmentmentioning

confidence: 99%

Advancing socio-technical systems thinking: A call for bravery

Davis¹,

Challenger²,

et al. 2014

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Socio-technical systems thinking has predominantly been applied to the domains of new technology and work design over the past 60 years. Whilst it has made an impact, we argue that we need to be braver, encouraging the approach to evolve and extend its reach. In particular, we need to: extend our conceptualization of what constitutes a system; apply our thinking to a much wider range of complex problems and global challenges; and engage in more predictive work. To illustrate our agenda in novel domains, we provide examples of socio-technical perspectives on the management of crowd events and environmental sustainability. We also outline a research and development agenda to take the area forward. Highlights A wider conceptualization of what constitutes a system, e.g., crowd events.  A wider range of applications, to include global challenges, e.g., sustainability.  Framework for more predictive analysis, e.g., to identify and mitigate risks.  Retrospective case studies to generate lessons for use predictively.  Research and development agenda to advance socio-technical practice. KeywordsSystems; Crowds; Sustainability; Prediction 3 IntroductionSocio-technical systems theory has enjoyed around 60 years of development and application internationally by both researchers and practitioners (e.g., Baxter & Sommerville, 2011; Carayon, 2006;Cherns, 1976Cherns, , 1987Clegg, 2000;Eason, 1988Eason, , 2007 Mumford, 1983Mumford, , 2006Pasmore & King, 1978;Trist & Bamforth, 1951;van Eijnatten, 1997;Waterson, 2005). The over-arching philosophy, embracing the joint design and optimization of organizational systems (incorporating both social and technical elements), has maintained its practical relevance and has seen increasing recognition and acceptance by audiences outside the social sciences (Eason, 2008). Such successes can be attributed, in part, to the continuing evolution of sociotechnical systems thinking and practice.In this paper, we argue that people engaged in socio-technical thinking need to extend their conceptualizations of 'systems', apply the core ideas to new domains reaching beyond the traditional focus on new technologies, and, at the same time, become involved in predictive work. The underlying call is to be braver in all three respects.To these ends, we first reflect upon the progress of socio-technical systems thinking to-date and the impact it has achieved. We then make the case for extending socio-technical systems theory to new domains. To do this, we describe a framework for socio-technical analyzis and design which we use to provide two illustrative examples of its application in novel settings. In each of the examples we identify the potential and importance of undertaking predictive work. We then outline a research and development agenda that will help take the domain forward. We argue that the application of socio-technical thinking to new areas may help address significant contemporary challenges, thereby extending our social impact and reach, and, at the same time, offering opportunit...

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Section: An Agenda For Research and Developmentmentioning

confidence: 99%

Advancing socio-technical systems thinking: A call for bravery

Davis¹,

Challenger²,

et al. 2014

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“…Levitt et al (1999) discuss a case study of satellite launch vehicle design, in which the VDT was used to evaluate the impact of proposed changes such as increasing individuals' skill levels and improving alignment of their objectives. Other ABMs developed for the DDP context include the Agent Model for Planning and rEsearch of eaRly dEsign (AMPERE), which focuses on studying the impact of requirements changes during design (Fernandes 2015), and the model of Crowder et al (2012), which focuses on the factors involved in effective team working.…”

Section: Agent-based Task Network Modelsmentioning

confidence: 99%

Process models in design and development

2017

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Many models of the design and development process have been published over the years, representing it for different purposes and from different points of view. This article contributes an organising framework that clarifies the topology of the literature on these models and thereby relates the main perspectives that have been developed. The main categories of model are introduced. Their contexts, advantages, and limitations are considered through discussion of selected examples. It is demonstrated that the framework integrates coverage of earlier reviews and as such provides a new perspective on the literature. Finally, key characteristics of design and development process models are discussed considering their applications in practice, and opportunities for further research are suggested. Overall, the article should aid researchers in positioning new models and new modelling approaches in relation to state-of-the-art. It may also be of interest to practitioners and educators seeking an overview of developments in this area.

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“…That work also obtained results that agreed qualitatively with the literature, but modeled the design problem as an abstract task network instead of directly solving a concrete design problem. Other work simulated with great detail the tasks involved in an integrated product development team, but did not apply the model to a real design task, or offer empirical validation (Crowder, Robinson, Hughes, & Sim, 2012).Regarding the simulation of individuals, simulated annealing (Kirkpatrick, Gelatt, & Vecchi, 1983), a stochastic optimization algorithm, has been used as an effective model for the efforts of individual human problem-solvers (Cagan & Kotovsky, 1997). More recent work has demonstrated the potential benefit of using computational agents to rapidly test and refine rule-based search strategies that can then be provided to human designers (Egan, Cagan, Schunn, & Leduc, 2014).…”

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confidence: 99%

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Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

2015

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Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena, thus balancing simplicity and direct applicability. This paper discusses the model's composition, and demonstrates its utility through simulating human design teams in a cognitive study. Simulation results are compared directly to the results from human designers. The CISAT model is also used to identify the most beneficial characteristics in the cognitive study. Keywords: computational model, design cognition, teamwork, engineering design Much cognitive research in engineering design has focused on individuals, despite the fact that most engineering design is actually performed by teams (Paulus, Dzindolet, & Kohn, 2011). This work focuses on developing a better understanding of team-based design through computationally simulating the team design process. Empirical studies are a common means for exploring design cognition and for testing new design methodologies. However, these studies can incur a high personnel cost while only returning a limited amount of data. It can also be difficult to isolate the effects of specific characteristics. This work introduces a computational framework that simulates teambased engineering design through creating software agents that directly solve engineering problems. In addition to offering a resource efficient test bed for evaluating design strategies, this framework can be used to test the conclusions from cognitive studies. It can be used to peel apart aspects of human design, and provides a succinct representation of designer behavior. The purpose of the framework is not to replace cognitive studies, but rather to augment traditional methods of investigation, accelerating the discovery of improved design methodologies.A significant amount of work has attempted to simulate the performance of both teams and individuals (Fan & Yen, 2004). For instance, both the Virtual Design Team model, and another model applied to teams at NASA's Jet Propulsion Laboratory, incorporate detailed descriptions of design team organization and interaction (Jin & Levit, 1996;Olson, Cagan, & Kotovsky, 2009). Both models were used to simulate complex design tasks, but were also burdened by high model complexity. For instance, the model by Olson et al. (2009) used approximately 1000 distinct variables, and required nearly 100000 lines of code for implementation. Still other work has utilized agent-based models to explore the formation of mental models during team problem-solving with respect to both interaction structure (Dionne, Sayama, Hao, & James, 2010) and agent memory (Sayama, Farrell, & Dionne, 2011). Mental ...

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The Development of an Agent-Based Modeling Framework for Simulating Engineering Team Work

Cited by 78 publications

References 50 publications

Advancing socio-technical systems thinking: A call for bravery

Advancing socio-technical systems thinking: A call for bravery

Process models in design and development

Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

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