Personality and Agents: Formalising State and Effects

Ahrndt, Sebastian; Trollmann, Frank; Fähndrich, Johannes; Albayrak, Şahin

doi:10.1007/978-3-319-45889-2_2

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Recently, researchers in agent-based modelling have begun using personality to determine agent behaviors. Early models have interpretations that loosely match psychology literature, are context dependent, and largely unverified (Ahrndt et al, 2015;Salvit and Sklar, 2012). More recently, Lim and Bentley (2018) created a more general, problemindependent computational model aiming to improve our understanding of the effects of personality on teamwork.…”

Section: Agent-based Models Of Personality In Collaborationmentioning

confidence: 98%

“…Statistical studies of data produced by individuals as they attempt different tasks are used to derive statistical models or psychological theories. Some attempts to use computer modelling have been made, but their predictions are largely unverified with data (Ahrndt et al, 2015;Bentley, 2018, 2019b;Salvit and Sklar, 2012). Neither approach explains teamwork adequately-the former suffers from a lack of data on teams and may not offer insights into why certain combinations of individuals may work better than others, while the latter suffers from a lack of rigorous corroboration.…”

Section: Introductionmentioning

confidence: 99%

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Kill chaos with kindness: Agreeableness improves team performance under uncertainty

Lim

Bentley

Peterson

et al. 2023

Collective Intelligence

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Teams are central to human accomplishment. Over the past half-century, psychologists have identified the Big-Five cross-culturally valid personality variables: Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness. The first four have shown consistent relationships with team performance. Agreeableness (being harmonious, altruistic, humble, and cooperative), however, has demonstrated a non-significant and highly variable relationship with team performance. We resolve this inconsistency through computational modelling. An agent-based model (ABM) is used to predict the effects of personality traits on teamwork, and a genetic algorithm is then used to explore the limits of the ABM in order to discover which traits correlate with best and worst performing teams for a problem with different levels of uncertainty (noise). New dependencies revealed by the exploration are corroborated by analyzing previously unseen data from one of the largest datasets on team performance to date comprising 3698 individuals in 593 teams working on more than 5000 group tasks with and without uncertainty, collected over a 10-year period. Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty. Combining evolutionary computation with ABMs in this way provides a new methodology for the scientific investigation of teamwork, making new predictions, and improving our understanding of human behaviors. Our results confirm the potential usefulness of computer modelling for developing theory, as well as shedding light on the future of teams as work environments are becoming increasingly fluid and uncertain.

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Section: Agent-based Models Of Personality In Collaborationmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Kill chaos with kindness: Agreeableness improves team performance under uncertainty

Lim

Bentley

Peterson

et al. 2023

Collective Intelligence

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show abstract

“…Finally, Openness represents individuals' behavioral preferences for being inventive, curious, and emotional. People with low Openness are less likely to experiment new ideas and experiences(SMITH et al, 2019;BARAŃCZUK, 2019;AHRNDT;FÄHNDRICH;ALBAYRAK, 2015).…”

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confidence: 99%

Personalify: uma estrutura para implementação de agentes MBTI.

Braz

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The use of Artificial Intelligence for the evolution of the study of human behavior has been receiving great attention from researchers over the years. The study of techniques that allow simulation of human characteristics using computers has contributed to a better understanding of several factors, including people's personality types and behavioral preferences. In this sense, instruments such as the Myers-Briggs Type Indicator (MBTI) have been used, seeking to classify and categorize individuals' behavioral preferences to describe their particularities and support a greater understanding of human factors in different contexts, such as in work. Studies show that human capital is one of the most critical resources for organizations that, despite this, still lack means that allow them to broadly understand the factors associated with the formation of high-performance teams. The use of tools that allow individuals greater self-knowledge of their preferences and characteristics can help in adaptations that create more harmonious environments and are favorable to teamwork, positively impacting teams' results. In this work, we sought to explore, through agent-based simulations, the observation of behavioral aspects that can influence decisions and the consequent performance of artificial agent teams. In particular, we propose a framework called Personalify, which extends the Belief-Desire-Intentions (BDI) architecture in order to incorporate multiple decision attributes designed with characteristics described in MBTI model. The Personalify was implemented on the Gama platform, and tested in various buyers and sellers scenarios, which have similarities with work contexts, aiming to provide simulations closer to organizational realities. In this way, it is expected that the study will contribute significantly to a better understanding of elements that can influence modeled agent behavior with personalities inspired by MBTI theory, observing how certain characteristics can have a greater impact on the decisions made by agents and how these could be related to overall results presented by the team.

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Personality and Agents: Formalising State and Effects

Cited by 2 publications

References 7 publications

Kill chaos with kindness: Agreeableness improves team performance under uncertainty

Kill chaos with kindness: Agreeableness improves team performance under uncertainty

Personalify: uma estrutura para implementação de agentes MBTI.

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