scholarly journals Emergence of Coordination in Growing Decision-Making Organizations: The Role of Complexity, Search Strategy, and Cost of Effort

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-26
Author(s):  
Friederike Wall
Keyword(s):  
Decision Making ◽  
Decision Problem ◽  
Search Strategy ◽  
Decision Makers ◽  
Agent Based ◽  
Distributed Decision Making ◽  
Human Decision ◽  
Nk Fitness Landscapes ◽  
The Cost ◽  
Different Levels

Coordination among decision-makers of an organization, each responsible for a certain partition of an overall decision-problem, is of crucial relevance with respect to the overall performance obtained. Among the challenges of coordination in distributed decision-making systems (DDMS) is to understand how environmental conditions like, for example, the complexity of the decision-problem to be solved, the problem’s predictability and its dynamics shape the adaptation of coordination mechanisms. These challenges apply to DDMS resided by human decision-makers like firms as well as to systems of artificial agents as studied in the domain of multiagent systems (MAS). It is well known that coordination for increasing decision-problems and, accordingly, growing organizations is in a particular tension between shaping the search for new solutions and setting appropriate constraints to deal with increasing size and intraorganizational complexity. Against this background, the paper studies the adaptation of coordination in the course of growing decision-making organizations. For this, an agent-based simulation model based on the framework of NK fitness landscapes is employed. The study controls for different levels of complexity of the overall decision-problem, different strategies of search for new solutions, and different levels of cost of effort to implement new solutions. The results suggest that, with respect to the emerging coordination mode, complexity subtly interferes with the search strategy employed and cost of effort. In particular, results support the conjecture that increasing complexity leads to more hierarchical coordination. However, the search strategy shapes the predominance of hierarchy in favor of granting more autonomy to decentralized decision-makers. Moreover, the study reveals that the cost of effort for implementing new solutions in conjunction with the search strategy may remarkably affect the emerging form of coordination. This could explain differences in prevailing coordination modes across different branches or technologies or could explain the emergence of contextually inferior modes of coordination.

scholarly journals Theoretical foundations of human decision-making in agent-based land use models – A review

2017 ◽  
Vol 87 ◽  
pp. 39-48 ◽  
Author(s):  
J. Groeneveld ◽  
B. Müller ◽  
C.M. Buchmann ◽  
G. Dressler ◽  
C. Guo ◽  
...  
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Agent Based ◽  
Human Decision ◽  
Land Use Models ◽  
Theoretical Foundations

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Studying the prominence effect amid the COVID-19 crisis: implications for public health policy decision-making.

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1356
Author(s):  
Yossi Maaravi ◽  
Ben Heller
Keyword(s):  
Decision Making ◽  
Policy Decision ◽  
Matching Condition ◽  
Decision Makers ◽  
The Other ◽  
Choice Condition ◽  
Health Concerns ◽  
Prominence Effect ◽  
Novel Coronavirus ◽  
The Cost

The novel coronavirus disease 2019 (COVID-19) has brought with it crucial policy- and decision-making situations, especially when making judgments between financial and health concerns. One particularly relevant decision-making phenomenon is the prominence effect, where decision-makers base their decisions on the most prominent attribute of the object at hand (e.g., health concerns) rather than weigh all the attributes together. This bias diminishes when the decision-making mode inhibits heuristic processes. In this study, we tested the prominence of health vs. financial concerns across two decision-making modes - choice (prone to heuristics) and matching (mitigates heuristics) - during the peak of the COVID-19 in the UK using Tversky et al.’s classic experimental paradigm. We added to the classic experimental design a priming condition. Participants were presented with two casualty-minimization programs, differing in lives saved and costs: program X would save 100 lives at the cost of 55-million-pound sterling, whereas program Y would save 30 lives at the cost of 12-million-pound sterling. Half of the participants were required to choose between the programs (choice condition). The other half were not given the cost of program X and were asked to determine what the cost should be to make it as equally attractive as the program Y. Participants in both groups were primed for either: a) financial concerns; b) health concerns; or c) control (no priming). Results showed that in the choice condition, unless primed for financial concerns, health concerns are more prominent. In the matching condition, on the other hand, the prominence of health concerns did not affect decision-makers, as they all “preferred” the cheaper option. These results add further support to the practical relevance of using the proper decision-making modes in times of consequential crises where multiple concerns, interests, and parties are involved.

scholarly journals MULTI CRITERIA DECISION MAKING – RANKING AND PROMOTING EMPLOYEES

2019 ◽  
Vol 31 (5) ◽  
pp. 1235-1241
Author(s):  
Marina Badarovska Mishevska
Keyword(s):  
Decision Making ◽  
Decision Problem ◽  
Decision Makers ◽  
Decision Making Process ◽  
Specific Situation ◽  
Mathematical Tool ◽  
Analytic Hierarchy ◽  
The Right ◽  
Term Performance ◽  
Hierarchy Process

The analytic hierarchy process (AHP) is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. The method was developed by Thomas L. Saaty in the 1970s and has been extensively studied and refined since then. It has particular application in group decision making and is used around the world in a wide variety of decision situation. Rather than prescribing a "correct" decision, the AHP helps decision makers choose one that best suits their goal and their understanding of the problem. The technique provides a comprehensive and rational framework for structuring a decision problem, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions. Decision making is the choice of one alternative, from two or more, to which the course of the activity is directed and the problem is solved. The decision-making process is a rational attempt by the manager to achieve the goals of the organizational unit. The decision-making process can be thought of as a "brain and nervous system" of an enterprise. Decisions are made when a person wants things to be different in the future. Given each specific situation, making the right decisions is probably one of the most difficult challenges for managers. Managers in day-to-day work deliver programmed and unprogrammed decisions that solve simple or complex problems. Simple decisions have an impact on the short-term performance of the enterprise, and complex decisions have an impact on the long-term future and success of the enterprise. Users of the AHP first decompose their decision problem into a hierarchy of more easily comprehended sub-problems, each of which can be analyzed independently. Once the hierarchy is built, the decision makers systematically evaluate its various elements by comparing them to each other two at a time, with respect to their impact on an element above them in the hierarchy. The AHP converts these evaluations to numerical values that can be processed and compared over the entire range of the problem. In this article, it is explained the application of the AHP method in order to evaluate and promote employees in the enterprise "X" with several criteria. The obtained results enable the manager to evaluate the employees in an objective way and make an objective decision for their promotion. Its application for selecting the best among employees, in their assessment and promotion, allows managers to use a specific and mathematical tool to support the decision. This tool not only supports and qualifies decisions, it also allows managers to justify their choice, as well as to simulate possible results.

An Approach of Decision-Making Support Based on Collaborative Agents for Unexpected Rush Orders Management

2011 ◽  
pp. 108-125
Author(s):  
El Habib Nfaoui ◽  
Omar El Beqqali ◽  
Yacine Ouzrout ◽  
Abdelaziz Bouras
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Problem Solving ◽  
Broad Class ◽  
Global Distribution ◽  
Distributed Architecture ◽  
Agent Based ◽  
Multi Agent ◽  
Different Levels ◽  
Decision Making Support

Decisions at different levels of the supply chain can no longer be considered independently, since they may influence profitability throughout the supply chain. This paper focuses on the interest of multi-agent paradigm for the collaborative coordination in global distribution supply chain. Multi-agent computational environments are suitable for a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving contexts. An agent-based distributed architecture is proposed for better management of rush unexpected orders. This paper proposes a first architecture validated by a real and industrial case.

scholarly journals Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions

2019 ◽  
Vol 23 (5) ◽  
pp. 2261-2278 ◽  
Author(s):  
Jin-Young Hyun ◽  
Shih-Yu Huang ◽  
Yi-Chen Ethan Yang ◽  
Vincent Tidwell ◽  
Jordan Macknick
Keyword(s):  
Decision Making ◽  
Risk Perception ◽  
Regional Scale ◽  
Agent Based ◽  
Natural Processes ◽  
Complex Adaptive ◽  
San Juan River ◽  
Human Decision ◽  
Decision Making Processes ◽  
Engineered Systems

Abstract. Managing water resources in a complex adaptive natural–human system is a challenge due to the difficulty of modeling human behavior under uncertain risk perception. The interaction between human-engineered systems and natural processes needs to be modeled explicitly with an approach that can quantify the influence of incomplete/ambiguous information on decision-making processes. In this study, we two-way coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) to address this challenge. The human decision-making processes is described in the ABM using Bayesian inference (BI) mapping joined with a cost–loss (CL) model (BC-ABM). Incorporating BI mapping into an ABM allows an agent's psychological thinking process to be specified by a cognitive map between decisions and relevant preceding factors that could affect decision-making. A risk perception parameter is used in the BI mapping to represent an agent's belief on the preceding factors. Integration of the CL model addresses an agent's behavior caused by changing socioeconomic conditions. We use the San Juan River basin in New Mexico, USA, to demonstrate the utility of this method. The calibrated BC-ABM–RiverWare model is shown to capture the dynamics of historical irrigated area and streamflow changes. The results suggest that the proposed BC-ABM framework provides an improved representation of human decision-making processes compared to conventional rule-based ABMs that do not take risk perception into account. Future studies will focus on modifying the BI mapping to consider direct agents' interactions, up-front cost of agent's decision, and upscaling the watershed ABM to the regional scale.

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.

scholarly journals Between technochauvinism and human-centrism: Can algorithms improve decision-making in democratic politics?

2021 ◽  
Author(s):  
Pascal D. König ◽  
Georg Wenzelburger
Keyword(s):  
Decision Making ◽  
Policy Instruments ◽  
Decision Makers ◽  
Democratic Politics ◽  
Cognitive Tasks ◽  
Political Decision ◽  
Human Decision ◽  
Established Democracies ◽  
Political Decision Making ◽  
Selection Of

AbstractThe promise of algorithmic decision-making (ADM) lies in its capacity to support or replace human decision-making based on a superior ability to solve specific cognitive tasks. Applications have found their way into various domains of decision-making—and even find appeal in the realm of politics. Against the backdrop of widespread dissatisfaction with politicians in established democracies, there are even calls for replacing politicians with machines. Our discipline has hitherto remained surprisingly silent on these issues. The present article argues that it is important to have a clear grasp of when and how ADM is compatible with political decision-making. While algorithms may help decision-makers in the evidence-based selection of policy instruments to achieve pre-defined goals, bringing ADM to the heart of politics, where the guiding goals are set, is dangerous. Democratic politics, we argue, involves a kind of learning that is incompatible with the learning and optimization performed by algorithmic systems.

scholarly journals Studying the prominence effect amid the COVID-19 crisis: implications for public health policy decision-making.

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1356
Author(s):  
Yossi Maaravi ◽  
Ben Heller
Keyword(s):  
Decision Making ◽  
Policy Decision ◽  
Matching Condition ◽  
Decision Makers ◽  
The Other ◽  
Choice Condition ◽  
Health Concerns ◽  
Prominence Effect ◽  
Novel Coronavirus ◽  
The Cost

The novel coronavirus disease 2019 (COVID-19) has brought with it crucial policy- and decision-making situations, especially when making judgments between economic and health concerns. One particularly relevant decision-making phenomenon is the prominence effect, where decision-makers base their decisions on the most prominent attribute of the object at hand (e.g., health concerns) rather than weigh all the attributes together. This bias diminishes when the decision-making mode inhibits heuristic processes. In this study, we tested the prominence of health vs. economic concerns across two decision-making modes - choice (prone to heuristics) and matching (mitigates heuristics) - during the peak of the COVID-19 in the UK using Tversky et al.’s classic experimental paradigm. We added to the classic experimental design a priming condition. Participants were presented with two casualty-minimization programs, differing in lives saved and costs: program X would save 100 lives at the cost of 55-million-pound sterling, whereas program Y would save 30 lives at the cost of 12-million-pound sterling. Half of the participants were required to choose between the programs (choice condition). The other half were not given the cost of program X and were asked to determine what the cost should be to make it as equally attractive as the program Y. Participants in both groups were primed for either: a) economic concerns; b) health concerns; or c) control (no priming). Results showed that in the choice condition, unless primed for economic concerns, health concerns are more prominent. In the matching condition, on the other hand, the prominence of health concerns did not affect decision-makers, as they all “preferred” the cheaper option. These results add further support to the practical relevance of using the proper decision-making modes in times of consequential crises where multiple concerns, interests, and parties are involved.

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