scholarly journals A multilevel and dynamic model of intragroup conflict and decision making: application of agent-based modeling

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Jiunyan Wu ◽  
Tomoki Sekiguchi
Keyword(s):  
Decision Making ◽  
Dynamic Model ◽  
Agent Based Modeling ◽  
Extended Model ◽  
Conflict Task ◽  
Holistic Model ◽  
Agent Based ◽  
Intragroup Conflict ◽  
Complex Relationships ◽  
Over Time

AbstractAlthough intragroup conflict has both multilevel and dynamic natures, less attention has been paid to establishing a holistic model of intragroup conflict that emerges across levels and unfolds over time. To address this research gap, we extend the multilevel view of intragroup conflict (Korsgaard et al. 2008) to develop a multilevel and dynamic model of intragroup conflict that explicitly includes (1) the role of time and (2) the feedback loop to encompass the dynamic aspect of intragroup conflict. We further instantiate the extended model in the context of team decision-making. To achieve this and systematically examine the complex relationships, we use agent-based modeling and simulation (ABMS). We directly investigate how two types of intragroup conflict—task and relationship conflict—interplay with cross-level antecedences, interrelate and develop over time, and affect team outcomes. This study adds to the intragroup conflict research by extending the field with multilevel and dynamic views.

Hybrid simulation modeling for humanitarian relief chain coordination

2015 ◽  
Vol 5 (3) ◽  
pp. 325-347 ◽  
Author(s):  
Caroline C Krejci
Keyword(s):  
Decision Making ◽  
Data Collection ◽  
Simulation Modeling ◽  
Model Development ◽  
Hybrid Simulation ◽  
Humanitarian Logistics ◽  
Content Type ◽  
Agent Based ◽  
Data Collection And Analysis ◽  
Over Time

Purpose – The purpose of this paper is to present a conceptual framework for a hybrid simulation model that can be used to study the decision making and behaviors of humanitarian logistics actors to determine how/whether certain coordination mechanisms enable better relief chain efficiency and effectiveness over time. Design/methodology/approach – The agent-based portion of the model is used to represent human decision making and interactions in a more realistic way than has been done previously, and the discrete-event simulation (DES) portion of the model allows the movement of vehicles, materials, and information throughout a supply network to be represented in a way that allows for dynamic and stochastic behavior. Findings – Coordinated efforts by actors in humanitarian logistics operations involve complex interactions and adaptations over time, which can be capture and explored via hybrid agent-based model (ABM)-DES modeling. Research limitations/implications – This paper describes a framework for a hybrid ABM-DES model. The actual development and implementation of the model, including input data collection and analysis, model development, experimentation, and output data collection and analysis, will be the subject of future work. Practical implications – The hybrid model framework provides other researchers with a starting point for model development. Social implications – This paper provides a basis for future modeling and assessment of coordination in humanitarian logistics, an area that is in need of research. Originality/value – The hybrid simulation modeling framework presented in this paper is a novel application of a new modeling methodology to the problem of coordination in humanitarian logistics.

scholarly journals Approaches to Modeling the Gas-Turbine Maintenance Process

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Tai-Tuck Yu ◽  
James P. Scanlan ◽  
Richard M. Crowder ◽  
Gary B. Wills
Keyword(s):  
Decision Making ◽  
Gas Turbine ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Event Modeling ◽  
Human Decision ◽  
Event Model ◽  
Discrete Event Model

Discrete-event modeling has long been used for logistics and scheduling problems, while multi-agent modeling closely matches human decision-making process. In this paper, a metric-based comparison between the traditional discrete-event and the emerging agent-based modeling approaches is reported. The case study involved the implementation of two functionally identical models based on a realistic, nontrivial, civil aircraft gas turbine global repair operation. The size, structural complexity, and coupling metrics from the two models were used to gauge the benefits and drawbacks of each modeling paradigm. The agent-based model was significantly better than the discrete-event model in terms of execution times, scalability, understandability, modifiability, and structural flexibility. In contrast, and importantly in an engineering context, the discrete-event model guaranteed predictable and repeatable results and was comparatively easy to test because of its single-threaded operation. However, neither modeling approach on its own possesses all these characteristics nor can each handle the wide range of resolutions and scales frequently encountered in problems exemplified by the case study scenario. It is recognized that agent-based modeling can emulate high-level human decision-making and communication closely while discrete-event modeling provides a good fit for low-level sequential processes such as those found in manufacturing and logistics.

Agent-Based Modeling in Political Decision Making

2020 ◽  
Author(s):  
Lin Qiu ◽  
Riyang Phang
Keyword(s):  
Decision Making ◽  
Boundary Conditions ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Political Systems ◽  
Political Decision ◽  
Theoretical Frameworks ◽  
Agent Based ◽  
Political Actors ◽  
Political Decision Making

Political systems involve citizens, voters, politicians, parties, legislatures, and governments. These political actors interact with each other and dynamically alter their strategies according to the results of their interactions. A major challenge in political science is to understand the dynamic interactions between political actors and extrapolate from the process of individual political decision making to collective outcomes. Agent-based modeling (ABM) offers a means to comprehend and theorize the nonlinear, recursive, and interactive political process. It views political systems as complex, self-organizing, self-reproducing, and adaptive systems consisting of large numbers of heterogeneous agents that follow a set of rules governing their interactions. It allows the specification of agent properties and rules governing agent interactions in a simulation to observe how micro-level processes generate macro-level phenomena. It forces researchers to make assumptions surrounding a theory explicit, facilitates the discovery of extensions and boundary conditions of the modeled theory through what-if computational experiments, and helps researchers understand dynamic processes in the real-world. ABM models have been built to address critical questions in political decision making, including why voter turnouts remain high, how party coalitions form, how voters’ knowledge and emotion affect election outcomes, and how political attitudes change through a campaign. These models illustrate the use of ABM in explicating assumptions and rules of theoretical frameworks, simulating repeated execution of these rules, and revealing emergent patterns and their boundary conditions. While ABM has limitations in external validity and robustness, it provides political scientists a bottom-up approach to study a complex system by clearly defining the behavior of various actors and generate theoretical insights on political phenomena.

Modeling Multiparty Competition

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Making ◽  
Adaptive Learning ◽  
Party System ◽  
Decision Rules ◽  
Agent Based Modeling ◽  
Party Competition ◽  
Strategic Analysis ◽  
New Approach ◽  
Agent Based ◽  
Autonomous Decision

This chapter begins with a brief discussion of the need for a new approach to modeling party competition. It then makes a case for the use of agent-based modeling to study multiparty competition in an evolving dynamic party system, given the analytical intractability of the decision-making environment, and the resulting need for real politicians to rely on informal decision rules. Agent-based models (ABMs) are “bottom-up” models that typically assume settings with a fairly large number of autonomous decision-making agents. Each agent uses some well-specified decision rule to choose actions, and there may be considerable diversity in the decision rules used by different agents. Given the analytical intractability of the decision-making environment, the decision rules that are specified and investigated in ABMs are typically based on adaptive learning rather than forward-looking strategic analysis, and agents are assumed to have bounded rather than perfect rationality. An overview of the subsequent chapters is also presented.

Simulation modeling of the smart city system: The concept, methods, and cases

2020 ◽  
Vol 19 (2) ◽  
pp. 226-250 ◽  
Author(s):  
V.L. Makarov ◽  
R.A. Bakhtizin ◽  
G.L. Beklaryan ◽  
A.S. Akopov
Keyword(s):  
Decision Making ◽  
Urban Planning ◽  
System Dynamics ◽  
Smart City ◽  
Simulation Modeling ◽  
Simulation Models ◽  
Agent Based Modeling ◽  
Decision Making Process ◽  
Agent Based ◽  
City System

Subject. The research investigates key processes of urban life and its maintenance, including food supply, infrastructure, fire security, quality and accessibility of medical services, etc. The article also discusses the creation of a system supporting the Smart City decision-making process. Objectives. The research develops methods and tools to manage the Smart City system through system dynamics and agent-based modeling. Methods. Using simulation modeling, namely system dynamics and agent-based modeling (supported via Powersim and AnyLogic), we evaluate how multiple guiding parameters influence crucial characteristics of the Smart City system. Results. We devised an approach to designing the Smart City system through methods of system dynamics and agent-based modeling (supported via Powersim and AnyLogic) intended to streamline the decision making process for reasonable urban planning. Conclusions and Relevance. We propose the consolidated architecture of the Smart City decision-making system integrating the simulation models, data storage and city monitoring subsystem. The article describes the cases of simulation models implemented via Powersim and AnyLogic to support rational urban planning. The simulation models will significantly improve the quality of urban environment, satisfy the demand for food products, provide access to healthcare services and ensure effective rescue actions in case of emergency.

Agent-Based Modelnig with Boundedly Rational Agents

2007 ◽  
pp. 225-245 ◽  
Author(s):  
E. Ebenhoh
Keyword(s):  
Experimental Data ◽  
Decision Making ◽  
Model Building ◽  
Theoretical Concept ◽  
Agent Based Modeling ◽  
Modeling Framework ◽  
Agent Based ◽  
Rational Agents ◽  
Decision Making Processes ◽  
Participatory Model

This chapter introduces an agent-based modeling framework for reproducing micro behavior in economic experiments. It gives an overview of the theoretical concept which forms the foundation of the framework as well as short descriptions of two exemplary models based on experimental data. The heterogeneous agents are endowed with a number of attributes like cooperativeness and employ more or less complex heuristics during their decision-making processes. The attributes help to distinguish between agents, and the heuristics distinguish between behavioral classes. Through this design, agents can be modeled to behave like real humans and their decision making is observable and traceable, features that are important when agent-based models are to be used in collaborative planning or participatory model-building processes.

A Decision-Making Model for Environmental Behavior in Agent-Based Modeling

2013 ◽  
pp. 152-160 ◽  
Author(s):  
Noelia Sánchez-Maroño ◽  
Amparo Alonso-Betanzos ◽  
Óscar Fontenla-Romero ◽  
Miguel Rodríguez-García ◽  
Gary Polhill ◽  
...  
Keyword(s):  
Decision Making ◽  
Environmental Behavior ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Decision Making Model

scholarly journals The Missing Link Between Memory and Reinforcement Learning

2020 ◽  
Vol 11 ◽  
Author(s):  
Christian Balkenius ◽  
Trond A. Tjøstheim ◽  
Birger Johansson ◽  
Annika Wallin ◽  
Peter Gärdenfors
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Sensory Information ◽  
Sufficient Evidence ◽  
Extended Model ◽  
Missing Link ◽  
Memory Mechanism ◽  
State Action ◽  
Vicarious Trial And Error ◽  
Over Time

Reinforcement learning systems usually assume that a value function is defined over all states (or state-action pairs) that can immediately give the value of a particular state or action. These values are used by a selection mechanism to decide which action to take. In contrast, when humans and animals make decisions, they collect evidence for different alternatives over time and take action only when sufficient evidence has been accumulated. We have previously developed a model of memory processing that includes semantic, episodic and working memory in a comprehensive architecture. Here, we describe how this memory mechanism can support decision making when the alternatives cannot be evaluated based on immediate sensory information alone. Instead we first imagine, and then evaluate a possible future that will result from choosing one of the alternatives. Here we present an extended model that can be used as a model for decision making that depends on accumulating evidence over time, whether that information comes from the sequential attention to different sensory properties or from internal simulation of the consequences of making a particular choice. We show how the new model explains both simple immediate choices, choices that depend on multiple sensory factors and complicated selections between alternatives that require forward looking simulations based on episodic and semantic memory structures. In this framework, vicarious trial and error is explained as an internal simulation that accumulates evidence for a particular choice. We argue that a system like this forms the “missing link” between more traditional ideas of semantic and episodic memory, and the associative nature of reinforcement learning.

scholarly journals Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 519
Author(s):  
Nicholas R. Magliocca
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Significant Proportion ◽  
Agent Based Modeling ◽  
Future Research ◽  
Learning Technology ◽  
Coupled Modeling ◽  
Agent Based ◽  
Food Energy ◽  
Data Limitations

The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain.

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