Exploring the Effect of Experience on Team Behavior: A Computational Approach

Perišić, Marija Majda; Štorga, Mario; Gero, John S.

doi:10.1007/978-3-030-05363-5_32

Cited by 13 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The agent-based computational model comprises cognitively rich, social agents (Perišić et al, 2019). To describe agents' mental models, the agents' knowledge space is modelled as a directed, threelayered network where each layer corresponds to a domain from the Function-Behaviour-Structure ontology (Gero, 1990), and contains nodes of its respective type (i.e.…”

Section: Modelmentioning

confidence: 99%

“…Figure 1. Agent's mental model (Perišić et al, 2019) Each agent's mental model consists of a portion of the knowledge space, and -as depicted in Figure 1 the agent's reasoning processes are modelled as an activation spreading over its mental model (Perišić et al, 2019). To indicate the associative relation between knowledge nodes (i.e.…”

Section: Modelmentioning

confidence: 99%

“…To enable performance evaluations and to enable comparisons with previous work (Perišić et al, 2019), the statistics regarding the number of steps needed to find a solution, the overall number of new links and structures created, as well as the number of distinct links and structures communicated by the team during each task are collected, Table 1. Following the first hypothesis (H1) and the results from previous work, it is expected that experts would require significantly fewer steps than novices to find a satisfying solution.…”

Section: Summary Statisticsmentioning

confidence: 99%

“…The agents are based on social and cognitive theories and are capable of learning, thus providing them with the capability to change over time, and offering a means to simulate and study the adaptation processes of design teams. In the previous work (Perišić et al, 2017;2019), the computational model was designed, implemented, and tested for its ability to capture the trends observed in the real world. Namely, the focus of previous studies was on the effect of experience and learning on the behaviour of stable teams.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

Perišić

Martinec

Štorga

et al. 2019

Proc. Int. Conf. Eng. Des.

View full text Add to dashboard Cite

This paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.

show abstract

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Summary Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

Perišić

Martinec

Štorga

et al. 2019

Proc. Int. Conf. Eng. Des.

View full text Add to dashboard Cite

show abstract

“…Singh and Casakin, (2015) and Singh (2009) investigated the effect of methods/tools on design team collaboration. Others explored team expertise, team experience on task performance (e.g., Gero and Kannengiesser, 2004;Perišić et al, 2019). Decision-making was examined by Sayama et al (2010), Dionne and Dionne (2008), and Singh et al (2011) by varying team structure and leadership type.…”

mentioning

confidence: 99%

A Computational Framework for Exploring the Socio-Cognitive Features of Teams and their Influence on Design Outcomes

Singh

Cascini

Casakin

et al. 2019

Proc. Int. Conf. Eng. Des.

View full text Add to dashboard Cite

The dynamics of design teams play a critical role in product development, mainly in the early phases of the process. This paper presents a conceptual framework of a computational model about how cognitive and social features of a design team affect the quality of the produced design outcomes. The framework is based on various cognitive and social theories grounded in literature. Agent-Based Modelling (ABM) is used as a tool to evaluate the impact of design process organization and team dynamics on the design outcome. The model describes key research parameters, including dependent, independent, and intermediates. The independent parameters include: duration of a session, number of times a session is repeated, design task and team characteristics such as size, structure, old and new members. Intermediates include: features of team members (experience, learning abilities, and importance in the team) and social influence. The dependent parameter is the task outcome, represented by creativity and accuracy. The paper aims at laying the computational foundations for validating the proposed model in the future.

show abstract

Modelling the Dynamics of Influence on Individual Thinking During Idea Generation in Co-design Teams

Singh¹,

McComb²,

Cascini³

2022

Design Computing and Cognition’20

View full text Add to dashboard Cite

Social influence is not evenly distributed in teams. Some individuals, referred to here as influencers, become more influential than others. Consequentially, these influencers play a significant role in shaping project performance. The current work simulates the presence of influencers during idea generation in co-design teams to better understand emergent socio-cognitive phenomena. Besides providing, a novel approach for modelling learning in concept generation the model highlights the results related to individual cognition during idea generation. Idea quality and exploration of design space are affected by the presence of influencers in design teams. Teams with no well-defined influencers produce solutions with high general exploration but less quality. In contrast, the agents in the teams with only one influencer produce solutions high quality than those teams with no influencers.

show abstract

Exploring the Effect of Experience on Team Behavior: A Computational Approach

Cited by 13 publications

References 18 publications

A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

A Computational Framework for Exploring the Socio-Cognitive Features of Teams and their Influence on Design Outcomes

Modelling the Dynamics of Influence on Individual Thinking During Idea Generation in Co-design Teams

Contact Info

Product

Resources

About