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 Agent-Based Modeling Demonstrates How Local Chemotactic Behavior Can Shape Biofilm Architecture

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Emily G. Sweeney ◽  
Andrew Nishida ◽  
Alexandra Weston ◽  
Maria S. Bañuelos ◽  
Kristin Potter ◽  
...  
Keyword(s):  
Helicobacter Pylori ◽  
Three Dimensional ◽  
Structural Features ◽  
Agent Based Modeling ◽  
Emergent Properties ◽  
Cellular Interactions ◽  
Biofilm Growth ◽  
Content Type ◽  
Agent Based ◽  
H Pylori

ABSTRACTBacteria are often found living in aggregated multicellular communities known as biofilms. Biofilms are three-dimensional structures that confer distinct physical and biological properties to the collective of cells living within them. We used agent-based modeling to explore whether local cellular interactions were sufficient to give rise to global structural features of biofilms. Specifically, we asked whether chemorepulsion from a self-produced quorum-sensing molecule, autoinducer-2 (AI-2), was sufficient to recapitulate biofilm growth and cellular organization observed for biofilms ofHelicobacter pylori, a common bacterial resident of human stomachs. To carry out this modeling, we modified an existing platform, Individual-based Dynamics of Microbial Communities Simulator (iDynoMiCS), to incorporate three-dimensional chemotaxis, planktonic cells that could join or leave the biofilm structure, and cellular production of AI-2. We simulated biofilm growth of previously characterizedH. pyloristrains with various AI-2 production and sensing capacities. Using biologically plausible parameters, we were able to recapitulate both the variation in biofilm mass and cellular distributions observed with these strains. Specifically, the strains that were competent to chemotax away from AI-2 produced smaller and more heterogeneously spaced biofilms, whereas the AI-2 chemotaxis-defective strains produced larger and more homogeneously spaced biofilms. The model also provided new insights into the cellular demographics contributing to the biofilm patterning of each strain. Our analysis supports the idea that cellular interactions at small spatial and temporal scales are sufficient to give rise to larger-scale emergent properties of biofilms.IMPORTANCEMost bacteria exist in aggregated, three-dimensional structures called biofilms. Although biofilms play important ecological roles in natural and engineered settings, they can also pose societal problems, for example, when they grow in plumbing systems or on medical implants. Understanding the processes that promote the growth and disassembly of biofilms could lead to better strategies to manage these structures. We had previously shown thatHelicobacter pyloribacteria are repulsed by high concentrations of a self-produced molecule, AI-2, and thatH. pylorimutants deficient in AI-2 sensing form larger and more homogeneously spaced biofilms. Here, we used computer simulations of biofilm formation to show that localH. pyloribehavior of repulsion from high AI-2 could explain the overall architecture ofH. pyloribiofilms. Our findings demonstrate that it is possible to change global biofilm organization by manipulating local cell behaviors, which suggests that simple strategies targeting cells at local scales could be useful for controlling biofilms in industrial and medical settings.

Rapid video assessment for monitoring testing facility fraud

2018 ◽  
Vol 35 (8) ◽  
pp. 1508-1518
Author(s):  
Rosembergue Pereira Souza ◽  
Luiz Fernando Rust da Costa Carmo ◽  
Luci Pirmez
Keyword(s):  
Processing Time ◽  
Threshold Value ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Video Assessment ◽  
Technical Requirements ◽  
Testing Facility ◽  
Rapid Processing ◽  
Temporal Differencing

Purpose The purpose of this paper is to present a procedure for finding unusual patterns in accredited tests using a rapid processing method for analyzing video records. The procedure uses the temporal differencing technique for object tracking and considers only frames not identified as statistically redundant. Design/methodology/approach An accreditation organization is responsible for accrediting facilities to undertake testing and calibration activities. Periodically, such organizations evaluate accredited testing facilities. These evaluations could use video records and photographs of the tests performed by the facility to judge their conformity to technical requirements. To validate the proposed procedure, a real-world data set with video records from accredited testing facilities in the field of vehicle safety in Brazil was used. The processing time of this proposed procedure was compared with the time needed to process the video records in a traditional fashion. Findings With an appropriate threshold value, the proposed procedure could successfully identify video records of fraudulent services. Processing time was faster than when a traditional method was employed. Originality/value Manually evaluating video records is time consuming and tedious. This paper proposes a procedure to rapidly find unusual patterns in videos of accredited tests with a minimum of manual effort.

Use of a sonification system for science learning by people who are blind

2016 ◽  
Vol 10 (4) ◽  
pp. 187-198 ◽  
Author(s):  
Orly Lahav ◽  
Nuha Chagab ◽  
Vadim Talis
Keyword(s):  
Complex Systems ◽  
Conceptual Knowledge ◽  
Science Curriculum ◽  
Low Cost ◽  
Agent Based Modeling ◽  
Learning System ◽  
Learning Ability ◽  
Content Type ◽  
Agent Based ◽  
K 12

Purpose The purpose of this paper is to examine a central need of students who are blind: the ability to access science curriculum content. Design/methodology/approach Agent-based modeling is a relatively new computational modeling paradigm that models complex dynamic systems. NetLogo is a widely used agent-based modeling language that enables exploration and construction of models of complex systems by programming and running the rules and behaviors. Sonification of variables and events in an agent-based NetLogo computer model of gas in a container is used to convey phenomena information. This study examined mainly two research topics: the scientific conceptual knowledge and systems reasoning that were learned as a result of interaction with the listen-to-complexity (L2C) environment as appeared in answers to the pre- and post-tests and the learning topics of kinetic molecular theory of gas in chemistry that was learned as a result of interaction with the L2C environment. The case study research focused on A., a woman who is adventitiously blind, for eight sessions. Findings The participant successfully completed all curricular assignments; her scientific conceptual knowledge and systems reasoning became more specific and aligned with scientific knowledge. Practical implications A practical implication of further studies is that they are likely to have an impact on the accessibility of learning materials, especially in science education for students who are blind, as equal access to low-cost learning environments that are equivalent to those used by sighted users would support their inclusion in the K-12 academic curriculum. Originality/value The innovative and low-cost learning system that is used in this research is based on transmittal of visual information of dynamic and complex systems, providing perceptual compensation by harnessing auditory feedback. For the first time the L2C system is based on sound that represents a dynamic rather than a static array. In this study, the authors explore how a combination of several auditory representations may affect cognitive learning ability.

scholarly journals Deep Reinforcement Learning Agent for S&P 500 Stock Selection

Axioms ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 130
Author(s):  
Tommi Huotari ◽  
Jyrki Savolainen ◽  
Mikael Collan
Keyword(s):  
Portfolio Management ◽  
Stock Selection ◽  
Real World Data ◽  
Data Set ◽  
Agent Based ◽  
Trading Systems ◽  
Learning Agent ◽  
Trading Agent ◽  
Risk Taker ◽  
Insight Into

This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems.

Predicting the voluntary donation to online content creators

2020 ◽  
Vol 120 (10) ◽  
pp. 1941-1957
Author(s):  
Futao Zhao ◽  
Zhong Yao
Keyword(s):  
Prediction Model ◽  
Performance Metrics ◽  
Impact Factors ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Modal Particles ◽  
Interpretive Analysis ◽  
Text Content ◽  
The Impact

PurposeThe purpose of this paper is to identify the impact factors that might influence audiences' voluntary donation to content creators on the online platforms, and to build an effective prediction model by considering both content and creator-related features.Design/methodology/approachThis study collected the real-world data of content consumption from Xueqiu.com and extracted both content and creator characteristics from the data set. The best donation prediction model based on such features was determined by evaluating four prevalent classifiers with various performance metrics. Furthermore, three feature selection methods were applied to validate the robustness of the constructed model, and then the predictability of different feature groups was examined. Finally, we conducted an interpretive analysis to identify relatively important predictors.FindingsThe experimental results show that the random classifier with all extracted features outperformed other built models and achieved excellent performance, indicating the usefulness of these factors in predicting the donations. Moreover, the predictability of content features was demonstrated to be relatively better than that of creator ones. Finally, several particularly important predictors were identified such as the number of modal particles in the article.Originality/valueThis study is among the first to investigate what factors might drive customers' voluntary donation to content contributors on social websites. Different from previous studies focusing on live video streaming, we expand the research vision by examining the donations to user-generated text content, calling for attention to other important topics in the burgeoning industry.

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.

Facilitating organizational learning through agent-based modeling and simulation experimentation

2016 ◽  
Vol 23 (6) ◽  
pp. 429-443 ◽  
Author(s):  
Saša Baškarada ◽  
Arvind Chandran ◽  
Mina Shokr ◽  
Christopher Stewart
Keyword(s):  
Organizational Learning ◽  
Modeling And Simulation ◽  
Agent Based Modeling ◽  
Knowledge Generation ◽  
Generation Process ◽  
Complex Environments ◽  
Content Type ◽  
Agent Based ◽  
Internal Knowledge

Purpose In addition to requiring high absorptive capacity, contemporary organizations operating in highly dynamic and complex environments also require the ability to create knowledge internally, within the organization. While the organizational learning (OL) literature has produced a plethora of theories and frameworks, there has been relatively little empirical research on specific mechanisms for internal knowledge generation. Accordingly, this paper aims to answer calls for more research on mechanisms for internal generation of organizational knowledge. Design/methodology/approach This paper is an in-depth case study in the Australian Defence Organisation (ADO). Findings The paper presents a cyclical eight-stage knowledge generation process and demonstrates how agent-based modeling and simulation (ABMS) may be used to facilitate OL. Originality/value By detailing an in-depth case study of an ABMS mechanism for internal knowledge generation in the ADO, this paper provides a novel and relevant contribution to the OL literature.

scholarly journals Understanding Lactobacillus paracasei and Streptococcus oralis Biofilm Interactions through Agent-Based Modeling

mSphere ◽  
2021 ◽  
Author(s):  
Linda Archambault ◽  
Sherli Koshy-Chenthittayil ◽  
Angela Thompson ◽  
Anna Dongari-Bagtzoglou ◽  
Reinhard Laubenbacher ◽  
...  
Keyword(s):  
Biofilm Formation ◽  
Oral Candidiasis ◽  
Agent Based Modeling ◽  
Cell Models ◽  
Complex Environments ◽  
Inhibitory Effects ◽  
Content Type ◽  
Agent Based ◽  
Streptococcus Oralis

We previously discovered a role of the oral commensal Streptococcus oralis as an accessory pathogen. S. oralis increases the virulence of Candida albicans infections in murine oral candidiasis and epithelial cell models through mechanisms which promote the formation of tissue-damaging biofilms. Lactobacillus species have known inhibitory effects on biofilm formation of many microbes, including Streptococcus species. Agent-based modeling has great advantages as a means of exploring multifaceted relationships between organisms in complex environments such as biofilms.

Post M&A innovation in family firms

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdul-Basit Issah
Keyword(s):  
Design Methodology ◽  
Family Firms ◽  
Empirical Support ◽  
Data Set ◽  
Resource Based View ◽  
Content Type ◽  
External Resources ◽  
Merger Wave ◽  
The Relationship ◽  
Better Than

PurposeThe paper empirically investigates how family firms appropriate acquired resources to become more innovative in the context of merger waves. It draws on resource-based view and the theory of first mover (dis)advantages to examine the implications of the timing of acquisitions on innovation in family firms.Design/methodology/approachThe paper uses a panel data set of Standard & Poor's (S&P) 500 manufacturing firms followed over a period of 31 years.FindingsThe study finds empirical support for the predictions that family firms are more able to utilize acquired resources better than nonfamily firms. Furthermore, targets acquired during the upswing of a merger wave are more valuable to family firms and associated with more innovation than for nonfamily firms.Originality/valueThe paper establishes that resources acquired during the upswing of a merger wave are more valuable, provide better resource synergies and impact innovation positively in family firms than nonfamily firms. Second, the paper makes an empirical contribution that family firms absorb external resources markedly differently and more efficiently than nonfamily firms. Third, the paper enhances a better understanding of the influence of family ownership on the relationship between acquisitions and innovation outputs.

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.

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