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.

Decision-making approach for complex problems management in a scarce human resources environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcilio Andrade ◽  
Dermeval Carinhana Jr
Keyword(s):  
Decision Making ◽  
Human Resources ◽  
Theoretical Perspective ◽  
Optimal Solution ◽  
Complex Problem ◽  
Theory Of Constraints ◽  
Content Type ◽  
Physical Constraints ◽  
Complex Problems ◽  
The Relationship

Purpose This purpose of this study is to structure complex problems to be solved with greater efficiency, optimising the relationship between root causes (RC) relevance of the problem and utilisation of human resources to treat them, minimising the use of manpower in problem-solving activity and thus contributing to greater productivity within organisations. Design/methodology/approach The authors built an approach under the concepts of theory of constraints and multiattribute and multiobjective decision-making methods that were applied in a real complex problem of the low development of Brazilian space industry, by theoretical perspective. Also, the authors submitted it in a simulation environment to assess in which situations it is successful considering number of problem’s RC, system complexity and number of people in the system. Findings The approach was successful on the real case, finding the optimal relationship between the RC relevance and the number of people involved to treat them. For certain complex problem inputs configurations, simulation results reveal that the approach is reliable obtaining more than 95% chance of success in finding the optimal relationship, when comparing with traditional prioritising methods. Originality/value This approach introduces an unprecedented way to locate and evaluate non-physical constraints within a system, which is used to determine RC relevance, as well as an unprecedented way of defining a single optimal solution for structuring a problem, considering the relevance of RC and the use of human resources. The approach is useful for organisations in general which often need managing complex problems with few resources.

scholarly journals Artificial intelligence, real-time feedback and workplace learning analytics to support in situ complex problem-solving: a commentary

2020 ◽  
Vol 37 (5) ◽  
pp. 267-277
Author(s):  
Maarten de Laat ◽  
Srecko Joksimovic ◽  
Dirk Ifenthaler
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real Time ◽  
Workplace Learning ◽  
Learning Analytics ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Feedback Systems ◽  
Content Type

PurposeTo help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).Design/methodology/approachComplex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).FindingsIn this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.Originality/valueThe commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

Making sense: unleashing social capital in interdisciplinary teams

2017 ◽  
Vol 2 (3) ◽  
pp. 118-133 ◽  
Author(s):  
Lauren H. Bryant ◽  
Sherry Booth Freeman ◽  
Alan Daly ◽  
Yi-Hwa Liou ◽  
Suzanne Branon
Keyword(s):  
Social Capital ◽  
Problem Solving ◽  
Interdisciplinary Approach ◽  
Complex Problem ◽  
Interdisciplinary Teams ◽  
Sense Making ◽  
Complex Problem Solving ◽  
Educational Innovations ◽  
Content Type ◽  
The Creation

Purpose Previous attempts to solve complex problems in the field of education have often focused on one disciplinary perspective. This impedes the creation of meaningful solutions and lasting change. While an interdisciplinary approach has the potential for complex problem solving, it has often proven difficult. The purpose of this paper is to apply social capital and sense-making lenses to facilitate complex problem-solving on a large, interdisciplinary, National Science Foundation funded team. Design/methodology/approach Social network analysis (SNA) and interviews allowed for the examination of the existing underlying social structures of the project team, and the ways in which these underlying structures were impacting the team’s ability to leverage its own social capital. Findings Findings demonstrated that decentralized, low levels of weekly and daily collaboration may constrain the team’s capacity for collective sense-making and its ability to achieve coherence around project goals. Practical implications Using SNA to systematically study the underlying network structure of a team, with the intention to use that data to drive change, can allow teams to shape their networks over time to allow for sense-making and successful collaborations. It may be that, while large teams are studying their intended phenomena, they should also make time to study themselves. Social implications Increasing the successfulness of large teams stands to positively impact researchers’ abilities to create workable solutions to intractable problems. Originality/value While SNA is a popular approach to understanding school districts and the spread of educational innovations, this study uses SNA to understand the creation of solutions and innovations.

CAN THE BIG 3 OVERTAKE TOYOTA? — A STUDY BASED ON THE THEORY OF CONSTRAINTS

2007 ◽  
Vol 06 (02) ◽  
pp. 145-157 ◽  
Author(s):  
SIMON WU ◽  
SAMUEL WANG ◽  
MAURICIO F. BLOS ◽  
H. M. WEE
Keyword(s):  
Problem Solving ◽  
Coming Out ◽  
Optimal Solution ◽  
Complex Problem ◽  
Theory Of Constraints ◽  
Root Cause ◽  
Thinking Process ◽  
Core Problem ◽  
Current Reality

Purpose — The aim of this paper is to provide answers to two significant questions. The first question is "what is the comprehensive action for the Big 3 to overtake Toyota Company?" The second question is "Can TOC (Theory of Constraints) really deal with this kind of complicated problem effectively?" Design/methodology/approach — In order to address this question and come out with a reasonable answer, this study uses the Theory of Constraints to discover the root causes and countermeasures for the Big 3 to break through their paradigms. Findings — It is worthwhile to highlight that we have demonstrated that a sophisticated case in global competition of the motor market can provide solutions with only four TOC logic trees. Furthermore, it is interesting to note that the four TOC logic trees fit perfectly well with each of the four problem solving steps in two aspects: (1) It provides a shortcut through mirror imaging process and (2) It enhances the clarity of the thinking process. Research limitations/implications — However, there remains some issues open for further exploration: (1) How can we make sure that the appropriate core problem(s) or root cause(s) has been identified in CRT (Current Reality Tree) and it is indeed the most meaningful one? (2) How can we proceed from CRT to FRT (Future Reality Tree) & further from FRT to PT (Prerequisites Tree) more effectively? (3) How can we discover key obstacles from PT and how to develop action plans from TT (Transition Tree) smoothly? (4) How to refine and integrate these feasible solution sets coming out from TT into the optimal solution scheme to be adapted in the real world? Originality/value — This study demonstrates how TOC problem solving can help to solve the core problems and root causes of "can the Big 3 overtake Toyota?" It not only gives managerial insights for the Big 3 to break through their paradigms to fight back Toyota; but also identify how a complex problem beyond production field can be analyzed and dealt with effectively. Paper type–Case study paper.

scholarly journals Agent-based simulation in management and organizational studies: a survey

2017 ◽  
Vol 26 (3) ◽  
pp. 313-328 ◽  
Author(s):  
Nelson Alfonso Gómez-Cruz ◽  
Isabella Loaiza Saa ◽  
Francisco Fernando Ortega Hurtado
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Organizational Behavior ◽  
Organizational Studies ◽  
Agent Based Simulation ◽  
Content Type ◽  
Agent Based ◽  
Rigorous Framework ◽  
Comprehensive Survey ◽  
Operational Decision Making

Purpose The purpose of this paper is to provide a comprehensive survey of the literature about the use of agent-based simulation (ABS) in the study of organizational behavior, decision making, and problem-solving. It aims at contributing to the consolidation of ABS as a field of applied research in management and organizational studies. Design/methodology/approach The authors carried out a non-systematic search in literature published between 2000 and 2016, by using the keyword “agent-based” to search through Scopus’ business, management and accounting database. Additional search criteria were devised using the papers’ keywords and the categories defined by the divisions and interest groups of the Academy of Management. The authors found 181 articles for this survey. Findings The survey shows that ABS provides a robust and rigorous framework to elaborate descriptions, explanations, predictions and theories about organizations and their processes as well as develop tools that support strategic and operational decision making and problem-solving. The authors show that the areas that report the highest number of applications are operations and logistics (37 percent), marketing (17 percent) and organizational behavior (14 percent). Originality/value The paper illustrates the increasingly prominent role of ABS in fields such as organizational behavior, strategy, human resources, marketing and logistics. To-date, this is the most complete survey about ABS in all management areas.

scholarly journals Searching far away from the lamp-post: An agent-based model

2016 ◽  
Vol 15 (2) ◽  
pp. 242-263 ◽  
Author(s):  
Oana Vuculescu
Keyword(s):  
Problem Solving ◽  
Optimal Solution ◽  
Experimental Setup ◽  
Problem Structure ◽  
Agent Based Model ◽  
Agent Based ◽  
Organizational Search ◽  
Simulation Results ◽  
Problem Solvers ◽  
Human Problem

This article presents insights from a laboratory experiment on human problem solving in a combinatorial task. I rely on a hierarchical rugged landscape to explore how human problem-solvers are able to detect and exploit patterns in their search for an optimal solution. Empirical findings suggest that solvers do not engage only in local and random distant search, but as they accumulate information about the problem structure, solvers make ‘model-based’ moves, a type of cognitive search. I then calibrate an agent-based model of search to analyse and interpret the findings from the experimental setup and discuss implications for organizational search. Simulation results show that, for non-trivial problems, performance can be increased by a low level of persistence, that is, an increased likelihood to quickly abandon unsuccessful paths.

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.

scholarly journals An optimization model of the acceptable consensus and its economic significance

Kybernetes ◽  
2016 ◽  
Vol 45 (1) ◽  
pp. 181-206 ◽  
Author(s):  
Zaiwu Gong ◽  
Xiaoxia Xu ◽  
Jeffrey Forrest ◽  
Yingjie Yang
Keyword(s):  
Information Entropy ◽  
Optimization Model ◽  
Optimal Solution ◽  
Limited Resource ◽  
Practical Significance ◽  
Content Type ◽  
Economic Significance ◽  
Consensus Opinion ◽  
Optimization Methodology ◽  
The Stability

Purpose – The purpose of this paper is to construct an optimal resource reallocation model of the limited resource by a moderator for reaching the greatest consensus, and show how to reallocate the limited resources by using optimization methodology once the consensus opinion is reached. Moreover, this paper also devotes to theoretically exploring when or what is the condition that the group decision-making (GDM) system is stable; and when new opinions enter into the GDM, how the level of consensus changes. Design/methodology/approach – By minimizing the differences between the individuals’ opinions and the collective consensus opinion, this paper constructs a consensus optimization model and shows that the objective weights of the individuals are actually the optimal solution to this model. Findings – If all individual deviations of the decision makers (DMs) from the consensus balance each other out, the information entropy theorem shows this GDM is most stable, and economically each individual DM gets the same optimal unit of compensation. Once the consensus opinion is determined and each individual opinion of the DMs is under an acceptable consensus level, the consensus is still acceptable even if additional DMs are added, and the moderator’s cost is still no more than a fixed upper limitation. Originality/value – The optimization model based on acceptable consensus is constructed in this paper, and its economic significance, including the theoretical and practical significance, is emphatically analyzed: it is shown that the weight information of the optimization model carries important economic significance. Besides, some properties of the proposed model are discussed by analyzing its particular solutions: the stability of the consensus system is explored by introducing information entropy theory and variance distribution; in addition, the effect of adding new DMs on the stability of the acceptable consensus system is discussed by analyzing the convergence of consensus level: it is also built up the condition that once the consensus opinion is determined, the consensus degree will not decrease even when additional DMs are added to the GDM.

scholarly journals SISTEM PENJADWALAN MATA PELAJARAN MENGGUNAKAN ALGORITMA GENETIKA

2019 ◽  
Vol 3 (1) ◽  
pp. 49
Author(s):  
Yesri Elva
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Problem Solving ◽  
Optimization Algorithm ◽  
Learning Process ◽  
Optimal Solution ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Algorithm Optimization ◽  
Fitness Value

Abstract - Schedule is one important factor to support the learning process, one of which at SMKN 3 Pariaman. In SMKN 3 Pariaman scheduling process is still done manually, consequently there are conflicting schedules and timing of learning becomes too late. One of completion method to the problem is to use a genetic algorithm, because it is one of the Genetic Algorithm optimization algorithm that is robust and can be used on a wide variety of case studies such as scheduling. This algorithm is also often used to find the optimal solution both in the case of simple to complex problem-solving technique that determines the start and initialization pupulasi chromosomes, determine the value of fitness, selection, crossover, mutation. Mutations done to produce the best fitness value which can be used to determine the final outcome scheduling. If the best fitness values have been obtained, the process is stopped and reach the finish condition.Keywords - Genetic Algorithms, Scheduling Abstrak - Jadwal merupakan salah satu faktor penting untuk penunjang proses belajar mengajar, salah satunya pada SMKN 3 Pariaman. Pada SMKN 3 Pariaman proses penyusunan jadwal masih dilakukan secara manual, akibatnya masih terdapat jadwal yang bentrok dan waktu pelaksanaan belajar mengajar menjadi terlambat. Salah satu metode untuk penyelesain masalah tersebut adalah dengan menggunakan algoritma genetika, karena Algoritma Genetika merupakan salah satu algoritma optimasi yang kuat dan bisa digunakan pada berbagai macam studi kasus seperti penjadwalan. Algoritma ini juga sering digunakan untuk mencari solusi optimal baik pada kasus yang sederhana sampai yang rumit teknik pemecahan masalahnya yaitu menentukan pupulasi awal dan inisialisasi kromosom, menentukan nilai fitness, seleksi crossover, mutasi. Mutasi dilakukan sampai menghasilkan nilai fitness terbaik yang dapat digunakan untuk penentuan hasil akhir penyusunan jadwal. Jika nilai fitness terbaik sudah didapatkan maka proses dihentikan dan mencapai kondisi selesai.Kata kunci  - Algoritma Genetika, Penjadwalan

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