Bayesian Decision Making in Groups is Hard

Hązła, Jan; Jadbabaie, Ali; Mossel, Elchanan; Rahimian, Mohammad Amin

doi:10.1287/opre.2020.2000

Cited by 23 publications

(7 citation statements)

References 45 publications

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“…Third, the refinement of the ε-greedy algorithm can be informed by studying or including other decision models, such as Bayesian SPRT, drift-diffusion, or adaptive gain models to determine stopping rules for exploration ( Tickle et al, 2021 ). Another possibility might be to use Bayesian network decision making models that incorporate agents holding private information and leveraging this for collective decisions ( Hązła et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

In no uncertain terms: Group cohesion did not affect exploration and group decision making under low uncertainty

et al. 2023

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Group decision making under uncertainty often requires groups to balance exploration of their environment with exploitation of the seemingly best option. In order to succeed at this collective induction, groups need to merge the knowledge of all group members and combine goal-oriented and social motivations (i.e., group cohesion). This paper presents three studies that investigate whether more cohesive groups perform worse at collective induction tasks as they spend less time exploring possible options. Study 1 simulates group decision making with the ε-greedy algorithm in order to identify suitable manipulations of group cohesion and investigate how differing exploration lengths can affect outcomes of group decisions. Study 2 (N = 108, 18 groups á 6 participants) used an experimental manipulation of group cohesion in a simple card choice task to investigate how group cohesion might affect group decision making when only limited social information is available. Study 3 (N = 96, 16 groups á 6 participants) experimentally manipulated group cohesion and used the HoneyComb paradigm, a movement-based group experiment platform, to investigate which group processes would emerge during decision making and how these processes would affect the relationships between group cohesion, exploration length, and group decision making. Study 1 found that multiplicative cohesion rewards have detrimental effects on group decision making, while additive group rewards could ameliorate negative effects of the cohesion reward, especially when reported separately from task rewards. Additionally, exploration length was found to profoundly affect decision quality. Studies 2 and 3 showed that groups could identify the best reward option successfully, regardless of group cohesion manipulation. This effect is interpreted as a ceiling effect as the decision task was likely too easy to solve. Study 3 identified that spatial group cohesion on the playing field correlated with self-reported entitativity and leader-/followership emerged spontaneously in most groups and correlated with self-reported perceptions of leader-/followership in the game. We discuss advantages of simulation studies, possible adaptations to the ε-greedy algorithm, and methodological aspects of measuring behavioral group cohesion and leadership to inform empirical studies investigating group decision making under uncertainty.

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Section: Discussionmentioning

confidence: 99%

In no uncertain terms: Group cohesion did not affect exploration and group decision making under low uncertainty

et al. 2023

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“…In this section, we illustrate how well the proposed algorithm is able to identify the true combination matrix A ⋆ and the expected log-likelihood matrix L. We also experiment with different M in (18) to see how the convergence rate changes. Additionally, we will see how well the learned combination matrix and KL-divergences identify the influences (30).…”

Section: Computer Simulationsmentioning

confidence: 99%

“…In principle, each agent in the network could consider pursuing a fully Bayesian solution to learn and track the state of nature. However, this solution is intractable and generally NP-hard [10], [11], [18]. This is because it requires that each agent has access to the data from the entire network, in addition to their knowledge of the full graph topology.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Discovering Influencers in Opinion Formation Over Social Graphs

Shumovskaia

Kayaalp

Cemri

et al. 2023

IEEE Open J. Signal Process.

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The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over the graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the graph topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents by using their public tweets (posts).

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“…A particularly important focus of previous research has been on the effects of social learning when repeated over successive groups of individuals, such as child–parent learning, formal education, and other domains where knowledge is transmitted from older members of a popvvvulation to younger learners. In these multigenerational settings, knowledge can accumulate over time in a population, allowing individuals to extend their cognitive skills by learning from others (see, e.g., Almaatouq, Alsobay, Yin, & Watts, 2021; Almaatouq et al., 2020; Belikov, Rzhetsky, & Evans, 2020; Caldwell, Atkinson, & Renner, 2016; Frey & Goldstone, 2018; Galesic, Olsson, Dalege, van der Does, & Stein, 2021; Goldstone, Wisdom, Roberts, & Frey, 2013; Hazła, Jadbabaie, Mossel, & Rahimian, 2021; Kempe & Mesoudi, 2014b; Mesoudi, 2016; Mesoudi & Thornton, 2018; Miton & Charbonneau, 2018; Riedl, Kim, Gupta, Malone, & Woolley, 2021; Rzhetsky, Foster, Foster, & Evans, 2015; Salhab, Ajorlou, & Jadbabaie, 2020; Wisdom, Song, & Goldstone, 2013; Wojtowicz & DeDeo, 2020, for recent overviews and related studies). Crucially, the constraints and structure of interpersonal transmission often lead collective knowledge and learning to differ from individual outcomes (Kirby, Tamariz, Cornish, & Smith, 2015; Ravignani, Thompson, Grossi, Delgado, & Kirby, 2018; Silvey, Kirby, & Smith, 2019).…”

Section: Introductionmentioning

confidence: 99%

Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations

Hardy¹,

Krafft

Thompson³

et al. 2022

Topics in Cognitive Science

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Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distinctive features of human intelligence. In this paper, we show that information accumulation in multigenerational social networks can be produced by a form of distributed Bayesian inference that allows individuals to benefit from the experience of previous generations while expending little cognitive effort. In doing so, we provide a criterion for assessing the rationality of a population that extends traditional analyses of the rationality of individuals. We tested the predictions of this analysis in two highly controlled behavioral experiments where the social transmission structure closely matched the assumptions of our model. Participants made decisions on simple categorization tasks that relied on and contributed to accumulated knowledge. Success required these microsocieties to accumulate information distributed across people and time. Our findings illustrate how in certain settings, distributed Mathew D. Hardy and P. M. Krafft contributed equally.

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Bayesian Decision Making in Groups is Hard

Abstract: Hardness of Making Rational Group Decisions

Cited by 23 publications

References 45 publications

In no uncertain terms: Group cohesion did not affect exploration and group decision making under low uncertainty

In no uncertain terms: Group cohesion did not affect exploration and group decision making under low uncertainty

Discovering Influencers in Opinion Formation Over Social Graphs

Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations

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