scholarly journals Attachment Preferences in Diverse Collective Problem-Solving Ventures and Systemic Performance

2021 ◽  
Author(s):  
Charles J. Gomez ◽  
Antonio Sirianni ◽  
Launy Schweiger
Keyword(s):  
Problem Solving ◽  
Information Seeking ◽  
Complex Problem ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Self Selection ◽  
Different Types ◽  
Individual Information

How do individual information-seeking preferences affect collective problem-solving in diverse settings? We often choose whom we collaborate with or seek information from. Self-selection can be driven by proclivities towards perceived merit or a preference for those who offer a different perspective. Yet our preferences can have profound systemic outcomes, even if opportunities abound to interact with diversity. We build upon the extensive tradition of collective problem-solving using agent-based modeling (ABM) to test this. We populate communicative networks of diverse problem-solving agents tasked with solving a complex problem too difficult to do alone. Agents can either exploit their neighbors’ solutions or explore for a new solution using their unique and diverse problem-solving ability. However, agents are also allowed to seek out new ties from the network. We test three conditions, where diverse agents in the network harbor proclivities towards (1) diversity (different types of neighbors), (2) homophily (same type of neighbors), or (3) merit (the current performance of their neighbors irrespective of type). We find that diversity-seeking not only leads to higher quality solutions, but also allows for these solutions to better disseminate to the rest of the network.

Iterative problem solving in teams: insights from an agent-based simulation

2016 ◽  
Vol 22 (1/2) ◽  
pp. 2-21 ◽  
Author(s):  
Aleksey Martynov ◽  
Dina Abdelzaher
Keyword(s):  
Problem Solving ◽  
Majority Rule ◽  
Optimal Solution ◽  
Complex Problem ◽  
Practical Significance ◽  
Problem Complexity ◽  
Content Type ◽  
Agent Based ◽  
Model Complex ◽  
Agent Based Simulations

Purpose – This paper aims to evaluate the effect of knowledge overlap, search width and problem complexity on the quality of problem-solving in teams that use the majority rule to aggregate heterogeneous knowledge of the team members. Design/methodology/approach – The paper uses agent-based simulations to model iterative problem-solving by teams. The simulation results are analyzed using linear regressions to show the interactions among the variables in the model. Findings – We find that knowledge overlap, search width and problem complexity interact to jointly impact the optimal solution in the iterative problem-solving process of teams using majority rule decisions. Interestingly, we find that more complex problems require less knowledge overlap. Search width and knowledge overlap act as substitutes, weakening each other’s performance effects. Research limitations/implications – The results suggest that team performance in iterative problem-solving depends on interactions among knowledge overlap, search width and problem complexity which need to be jointly examined to reflect realistic team dynamics. Practical implications – The findings suggest that team formation and the choice of a search strategy should be aligned with problem complexity. Originality/value – This paper contributes to the literature on problem-solving in teams. It is the first attempt to use agent-based simulations to model complex problem-solving in teams. The results have both theoretical and practical significance.

scholarly journals Clustering knowledge and dispersing abilities enhances collective problem solving in a network

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Charles J. Gomez ◽  
David M. J. Lazer
Keyword(s):  
Problem Solving ◽  
Agent Based Modeling ◽  
Social Structures ◽  
Cognitive Diversity ◽  
Agent Based ◽  
Local Homogeneity ◽  
Local Diversity ◽  
Long Run ◽  
Social Settings ◽  
Deliberative Groups

Abstract Diversity tends to generate more and better ideas in social settings, ranging in scale from small-deliberative groups to tech-clusters and cities. Implicit in this research is that there are knowledge-generating benefits from diversity that comes from mixing different individuals, ideas, and perspectives. Here, we utilize agent-based modeling to examine the emergent outcomes resulting from the manipulation of how diversity is distributed and how knowledge is generated within communicative social structures. In the context of problem solving, we focus on cognitive diversity and its two forms: ability and knowledge. For diversity of ability, we find that local diversity (intermixing of different agents) performs best at all time scales. However, for diversity of knowledge, we find that local homogeneity performs best in the long-run, because it maintains global diversity, and thus the knowledge-generating ability of the group, for a longer period.

Agent-Based Modeling of Collaborative Problem Solving

2016 ◽  
Vol 2016 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Yoav Bergner ◽  
Jessica J. Andrews ◽  
Mengxiao Zhu ◽  
Joseph E. Gonzales
Keyword(s):  
Problem Solving ◽  
Agent Based Modeling ◽  
Collaborative Problem Solving ◽  
Agent Based ◽  
Collaborative Problem

VALIDATION AND AGENT-BASED MODELING: A PRACTICE OF CONTRASTING SIMULATION RESULTS WITH EMPIRICAL DATA

2011 ◽  
Vol 07 (03) ◽  
pp. 515-542 ◽  
Author(s):  
FRANK C. S. LIU
Keyword(s):  
Statistical Analysis ◽  
External Validity ◽  
Science Communication ◽  
Perfect Matching ◽  
External Validation ◽  
Agent Based Modeling ◽  
Communication Studies ◽  
Agent Based ◽  
Different Types ◽  
Simulation Results

As an emerging approach to explore the dynamics of voter preference, agent-based modeling (ABM) highlights new opportunities for intellectual exchange across disciplines, such as mathematics, political science, communication studies, and computer science. By aiming to contribute to cross-disciplinary communication for a better application of this approach, this paper summarizes what scholars have done about internal and external validation and presents a comparison between statistical analysis based on datasets generated in a laboratory and analysis based on corresponding empirical datasets. The results of the comparison suggest that, although there is no perfect matching, the comparison reveals some similarities in terms of increase or decrease in the proportion of different types of agents. This result further implies that an internally valid ABM model may lead to a certain level of external validity.

scholarly journals Team problem solving and motivation under disorganization – an agent-based modeling approach

2017 ◽  
Vol 23 (1/2) ◽  
pp. 46-65 ◽  
Author(s):  
Dinuka Herath ◽  
Joyce Costello ◽  
Fabian Homberg
Keyword(s):  
Problem Solving ◽  
Agent Based Modeling ◽  
Hierarchical Level ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Non Profit ◽  
Agent Based ◽  
Prime Objective ◽  
Better Than

Purpose This paper aims at simulating on how “disorganization” affects team problem solving. The prime objective is to determine how team problem solving varies between an organized and disorganized environment also considering motivational aspects. Design/methodology/approach Using agent-based modeling, the authors use a real-world data set from 226 volunteers at five different types of non-profit organizations in Southwest England to define some attributes of the agents. The authors introduce the concepts of natural, structural and functional disorganization while operationalizing natural and functional disorganization. Findings The simulations show that “disorganization” is more conducive for problem solving efficiency than “organization” given enough flexibility (range) to search and acquire resources. The findings further demonstrate that teams with resources above their hierarchical level (access to better quality resources) tend to perform better than teams that have only limited access to resources. Originality/value The nuanced categories of “(dis-)organization” allow us to compare between various structural limitations, thus generating insights for improving the way managers structure teams for better problem solving.

scholarly journals Tolerance for failure unleashes the benefits of cognitive diversity in collective problem solving

2021 ◽  
Author(s):  
Justin Sulik ◽  
Bahador Bahrami ◽  
Ophelia Deroy
Keyword(s):  
Problem Solving ◽  
Learning Strategies ◽  
Complex Problem ◽  
Cognitive Diversity ◽  
Homogeneous Groups ◽  
Agent Based ◽  
Individual Level ◽  
Diverse Learning ◽  
Science Group ◽  
Diverse Groups

Collective problem solving is supposed to benefit from cognitive diversity, for instance when a team consists of individuals with different learning strategies. However, some recent evidence offered for this claim fails to rule out an alternative explanation: that the benefit is due to moderate non-conformity, rather than diversity of learning strategies. We extend a previous agent-based simulation to be able to distinguish these hypotheses, and demonstrate that diverse learning strategies alone do not yield the expected benefit. We then extend the model further, based on an idea from the philosophy of science: Group-level benefits in complex problem solving often entail individual-level failures. Accordingly, we parameterize tolerance for failure in a second batch of simulations, and show that there is an interaction between individual tolerance for failure and the diversity of learning strategies in the group. When tolerance for failure is zero, heterogeneous and homogeneous groups perform equally. However, when agents accept some individual-level failure, diverse groups can outperform heterogeneous groups, especially in noisy environments. Thus, our agent-based simulations help clarify when cognitive diversity is most likely to benefit collective problem solving.

scholarly journals Conceptualization of Vehicle-to-Grid Contract Types and Their Formalization in Agent-Based Models

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Esther H. Park Lee ◽  
Zofia Lukszo ◽  
Paulien Herder
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Supply And Demand ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Vehicle To Grid ◽  
Fuel Cell Electric Vehicles ◽  
Different Types ◽  
Wholesale Electricity Market ◽  
And Control

Fuel cell electric vehicles (FCEVs) have the potential to be used as flexible power plants in future energy systems. To integrate FCEVs through vehicle-to-grid (V2G), agreements are needed between the FCEV owners and the actor that coordinates V2G on behalf of them, usually considered the aggregator. In this paper, we argue that, depending on the purpose of providing V2G and the goal of the system or the aggregator, different types of contracts are needed, not currently considered in the literature. We propose price-based, volume-based, and control-based contracts. Using agent-based modeling and simulation we show how price-based contracts can be applied for selling V2G in the wholesale electricity market and how volume-based contracts can be used for balancing the local energy supply and demand in a microgrid. The models can provide a base to explore strategies in the market and to improve performance in a system highly dependent on V2G.

Complex Problem Solving and Worked Examples

2009 ◽  
Vol 23 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Florian Schmidt-Weigand ◽  
Martin Hänze ◽  
Rita Wodzinski
Keyword(s):  
Problem Solving ◽  
Cognitive Load ◽  
Learning Experience ◽  
Worked Examples ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Strategic Learning ◽  
Learning Behavior ◽  
Worked Example ◽  
8Th Grade

How can worked examples be enhanced to promote complex problem solving? N = 92 students of the 8th grade attended in pairs to a physics problem. Problem solving was supported by (a) a worked example given as a whole, (b) a worked example presented incrementally (i.e. only one solution step at a time), or (c) a worked example presented incrementally and accompanied by strategic prompts. In groups (b) and (c) students self-regulated when to attend to the next solution step. In group (c) each solution step was preceded by a prompt that suggested strategic learning behavior (e.g. note taking, sketching, communicating with the learning partner, etc.). Prompts and solution steps were given on separate sheets. The study revealed that incremental presentation lead to a better learning experience (higher feeling of competence, lower cognitive load) compared to a conventional presentation of the worked example. However, only if additional strategic learning behavior was prompted, students remembered the solution more correctly and reproduced more solution steps.

The Prediction of Problem-Solving Assessed Via Microworlds

2016 ◽  
Vol 32 (4) ◽  
pp. 298-306 ◽  
Author(s):  
Samuel Greiff ◽  
Katarina Krkovic ◽  
Jarkko Hautamäki
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Complex Problem ◽  
Two Dimensions ◽  
Assessment Instruments ◽  
Complex Problem Solving ◽  
Visual Spatial ◽  
Rule Knowledge ◽  
Rule Application ◽  
Fluid Reasoning

Abstract. In this study, we explored the network of relations between fluid reasoning, working memory, and the two dimensions of complex problem solving, rule knowledge and rule application. In doing so, we replicated the recent study by Bühner, Kröner, and Ziegler (2008) and the structural relations investigated therein [ Bühner, Kröner, & Ziegler, (2008) . Working memory, visual-spatial intelligence and their relationship to problem-solving. Intelligence, 36, 672–680]. However, in the present study, we used different assessment instruments by employing assessments of figural, numerical, and verbal fluid reasoning, an assessment of numerical working memory, and a complex problem solving assessment using the MicroDYN approach. In a sample of N = 2,029 Finnish sixth-grade students of which 328 students took the numerical working memory assessment, the findings diverged substantially from the results reported by Bühner et al. Importantly, in the present study, fluid reasoning was the main source of variation for rule knowledge and rule application, and working memory contributed only a little added value. Albeit generally in line with previously conducted research on the relation between complex problem solving and other cognitive abilities, these findings directly contrast the results of Bühner et al. (2008) who reported that only working memory was a source of variation in complex problem solving, whereas fluid reasoning was not. Explanations for the different patterns of results are sought, and implications for the use of assessment instruments and for research on interindividual differences in complex problem solving are discussed.

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