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Glynn, Peter W., Henrich R. Greve, and Hayagreeva Rao. "Relining the garbage can of organizational decision-making: modeling the arrival of problems and solutions as queues." Industrial and Corporate Change 29, no. 1 (December 12, 2019): 125–42. http://dx.doi.org/10.1093/icc/dtz069.
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Abstract The garbage can account of organizations where problems, solutions, and people chase each other is often invoked but rarely studied since its publication 44 years ago. It has been critiqued for being a metaphor rather than a model, and offering a deterministic rather than stochastic account. We reline the garbage can model of organizational decision-making by modeling the arrival of problems, people, and solutions as queues that get matched randomly. We show that queuing models allow us to understand the effect of using either experts, supervisor approval, teams, and deviation from supervision on problem resolution and oversight. Our approach shows that manager approval increased the standard deviation of problem resolution, whereas queues are processed faster and have lower variance when there is oversight or teamwork, or when manager approval is bypassed due to independent action by problem solvers. It also shows the costs of using an organizational hierarchy to address problems with different levels of difficulty, or specialization to address a mixture of fundamentally different problems. Thus, a stochastic garbage can model provides insights into why organizations make many decisions but often fail to resolve problems!
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Hoelscher, Carrisa S., Michael W. Kramer, Christopher Nguyen, Olivia D. Cooper, and Eric Anthony Day. "Decision Making and Communication in a Statewide Interagency Task Force." Management Communication Quarterly 31, no. 1 (August 1, 2016): 39–68. http://dx.doi.org/10.1177/0893318916661762.
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This study examines the proposed and utilized decision-making processes of an interagency taskforce formed to create a strategic plan for addressing substance abuse concerns. Analysis of data obtained through prolonged observation, interviews, and document collection indicated that, although the planned structure remained relatively intact, the taskforce deviated from planned decision-making processes in the procedures and decision-making criteria utilized. These deviations were justified through retrospective rationality and strategic ambiguity. Although prior research has described decision making using rational, satisficing, and garbage can models, the theoretical implications of this study point to a renewed understanding of collaborative decision making combining these approaches. Ultimately, this study illustrates how the characteristics of a loosely coupled, bona fide interorganizational group both enabled and constrained the decision-making process. Accordingly, practitioners and scholars alike should consider the advantages and limitations of retrospective rationality and strategic ambiguity across a variety of group and organizational contexts.
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Ocasio, William, Luke Rhee, and Dylan Boynton. "March and the pursuit of organizational intelligence: the interplay between procedural rationality and sensible foolishness." Industrial and Corporate Change 29, no. 1 (November 28, 2019): 225–39. http://dx.doi.org/10.1093/icc/dtz068.
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Abstract March’s long and varied career in organization theory encompasses a number of seemingly disparate themes from rationality, to ambiguity and the garbage can model, to exploration and exploitation in organizations. We examine March’s diverse research trajectory and conclude that his different insights can be brought together under one common theme for his career: that both procedural rationality and sensible foolishness are necessary for the pursuit of organizational intelligence. Traditional models of rationality, even bounded rationality, are insufficient because goals are unstable and inconsistent, and causal ambiguity leads to myopic learning or worse. To explain the interplay between procedural rationality and sensible foolishness in organizations, we explore their role in the inter-related processes of programing, monitoring, sensemaking, search, and decision making.
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Fardal, Harald, and Jan-Oddvar Sørnes. "IS Strategic Decision-Making: A Garbage Can View." Issues in Informing Science and Information Technology 5 (2008): 553–69. http://dx.doi.org/10.28945/1028.
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Saleri, N. G. "Re-Engineering Simulation: Managing Complexity and Complexification in Reservoir Projects." SPE Reservoir Evaluation & Engineering 1, no. 01 (February 1, 1998): 5–11. http://dx.doi.org/10.2118/36696-pa.
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Summary Managing complexity and technological complexification is a necessity in today's business environment. This paper outlines a method to increase value addition significantly by multidisciplinary reservoir studies. In this context, value addition refers to a positive impact on a business decision. The approach ensures a level of complexification in line both with business questions at hand and the realities of reservoirs. Sparse well control, seismic uncertainties, imperfect geologic models, time constraints, software viruses, and computing hardware limitations represent some common reservoir realities. The process model detailed in the paper uses these apparent shortcomings to moderate (i.e., guide) the level of complexification. Several project examples illustrate the implementation of the process model. The paper is an extension of three previous investigations1–3 that deal with issues of method and uncertainty in reservoir-performance forecasting. Introduction Multidisciplinary teams and data have become the standard 1990's methods to address large-scale reservoir-management issues. Concurrently, reservoir simulation has assumed the role as a "knowledge manager" of ever-growing quantities of information. The paper pursues three basic questions:How can we maximize the value added from integrated reservoir studies,How can we achieve a pragmatic balance between business objectives/timetables and problem complexification, andHow best can we use the technology dividend provided by the explosion of computing power Primarily because of their size, Saudi Arabian fields amplify the significance of these three questions. What has emerged is the realization that reservoir simulation needs to provide a proper demarcation between scientific and business objectives to remain business-relevant. The discussion that follows consists of two main parts. First, we present an analysis of complexity in general and reservoir systems in particular. This is followed by a process model (i.e., parallel planning plus) and a set of principles that link business needs, reservoir realities, and simulation in the context of multidisciplinary studies. The following definitions will facilitate the discussion that follows. Complex (adjective): Composed of interconnected parts. Complexity: The state of being intricate. The degree of interconnection among various parts. Complexification: The process of adding incremental levels of complexity to a system. Detail vs. Dynamic Complexity A vast array of multisourced information makes up reservoir systems (Fig. 1). Reservoir simulation is our attempt to link the "detail complexity" of such a system to the "dynamic complexity"4,5 expected in business decisions. In this regard, a systems engineering perspective to reservoir management is very relevant. Senge4 defines two types of complexity: detail and dynamic. Detail complexity entails defining individual ingredients in fine detail, while dynamic complexity refers to the dynamic, often unpredictable, outcomes of the interactions of the individual components. Senge4 states that "the real leverage in most management situations lies in understanding dynamic complexity, not detail complexity." This is precisely true for many of the questions facing reservoir-management project teams in the industry. When to initiate an EOR project or pattern realignment or how to develop a field are typical dynamic complexity problems. Relative-permeability data, field-management strategies, or wellbore hydraulics are examples of detail complexity. Geologic, geostatistical, and reservoir-simulation models are also examples of detail complexity, but represent higher orders of organization. Interestingly, reservoir-simulation models have a dual function: first, as an organizer of detail complexity, and, second, as a tool for interpreting dynamic complexity (a distinction from geologic models). Technological complexification is the process of adding incremental levels of detail complexity to a system to represent its dynamic complexity more rigorously. Each one of the components depicted in Fig. 1 offers an avenue of complexification. Perhaps ironically, every component also carries an element of uncertainty. New technologies are adding significantly to the detail complexity available to multidisciplinary teams. One can see that advances in computing technology, for instance, play a role in the cycle of complexification that Fig. 2 shows. As we acquire more computing power, we can build more complex models, which will further delineate the questions being addressed, calling for more computing power, and so on. The real question, however, is whether we are in fact getting a better answer to the questions posed. Or, alternatively, are we making a difference? Multidisciplinary studies are vulnerable to the tendency towards maximal detail complexity. As one of the constituent disciplines (e.g., seismic, geostatistics) produces a more detailed reservoir representation, the pressure mounts for the other disciplines to match the level of complexification in their respective areas. However, for many reservoir problems, we may have a nonlinear relationship between dynamic and detail complexity (Fig. 3). As the number of detail complexity elements rise, the number of interactions among the elements proliferate. Any one of these interactions can be a show stopper. For example, reservoir-simulation models constructed at the detail level (i.e., scale) of geocellular models can become numerically unstable or prohibitively central-processing-unit (CPU) intensive - either way, a nonsolution. Complexification vs. Error Expectations The reservoir system depicted in Fig. 1 does not represent a controlled data environment; i.e., we are not operating in a setting where we can control the quality and quantity (sufficiency) of data. Therefore, in reservoir systems, the concept of "garbage in/garbage out," when taken literally, is an oxymoron. There is always some contamination (error or uncertainty) in one of the detail complexity elements. Thus, we need to redefine our mission as "given the data environment as is, what is an acceptable error, and what is an appropriate level of complexification?"
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Tamir, Emanuel, and Mirit K. Grabarski. "Surviving the reform: management usage of the garbage can model during implementation of reform." Journal of Educational Administration 58, no. 3 (April 15, 2020): 373–87. http://dx.doi.org/10.1108/jea-09-2019-0169.
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PurposeThis paper aims to apply the garbage can model to identify factors that affect managerial decision-making processes in educational systems undergoing reforms.Design/methodology/approachThis paper used a qualitative approach using semi-structured interviews with 39 teachers and managers in schools undergoing a system-wide reform.FindingsThe paper presents examples for a typology of decision outcomes found in the model and provides explanations for their emergence. It shows that there are many challenges that are associated with reform implementation and suggests factors that need to be taken into account when planning and implementing a reform.Originality/valueSchool management and policy makers can learn about the risks that are associated with garbage can decision-making and the various risk factors. Practical suggestions are given to reduce the probability of suboptimal decision-making.
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Tamir, Emanuel, and Mirit K. Grabarski. "Searching for Gold in The Garbage Can: Decision-Making on Resource Utilization in Schools Using the Garbage-Can Model." Leadership and Policy in Schools 18, no. 3 (April 5, 2018): 325–40. http://dx.doi.org/10.1080/15700763.2018.1450515.
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Cohen, Sandra, and Sotirios Karatzimas. "Modernizing government accounting standards in Greece: a case of ‘garbage can’ decision-making." Public Money & Management 36, no. 3 (February 3, 2016): 173–80. http://dx.doi.org/10.1080/09540962.2016.1133966.
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Mitchell, Debbielou. "In and Out of the Garbage Can: A Model of Faculty Decision Making." Nursing Forum 22, no. 1 (January 1985): 12–15. http://dx.doi.org/10.1111/j.1744-6198.1985.tb00763.x.
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Fumagalli, Corrado. "When Do They Speak? Deliberation and Democratic Decision-Making in the European Union." Political Studies 67, no. 4 (March 25, 2019): 1053–71. http://dx.doi.org/10.1177/0032321719828276.
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In this article, I argue that the experimentalist model of democracy can contribute to contemporary disputes about deliberation at the supranational level. The fundamental idea is that, in conditions of disagreement, for a decision to be legitimate, deliberative decision-making processes must be structured so as to allow the inclusion of affected interests before and after voting. I argue that there are three ways for a decision to be illegitimate: exclusion of affected interests from all deliberative phases, Captain Hook politics and garbage-time politics. Captain Hook politics and garbage-time politics illuminate an important variable: in a deliberative process, some interests may enter deliberation too early, other interests too late. However, for a decision to be legitimate, it is not only important that all affected interests can have an influence on collectively binding decisions, but it is also important what moment in time such interests play a part in the deliberative process.
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Egli, Benjamin Thomas, Torsten Schlesinger, Mariëlle Splinter, and Siegfried Nagel. "Decision-making processes in football clubs associated with an external advisory programme." Sport, Business and Management: An International Journal 6, no. 4 (September 12, 2016): 386–406. http://dx.doi.org/10.1108/sbm-02-2015-0005.
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Purpose The purpose of this paper is to foster a better understanding of how decision-making processes work in sport clubs and to develop appropriate advisory concepts or management tools in order to successfully realize structural changes in sport clubs. This paper examines the decision-making processes associated with an external advisory programme. Based on the assumption of bounded rationality, the garbage can model is used to grasp these decision-making processes theoretically and to access them empirically. Design/methodology/approach Based on a case study framework, an in-depth analysis of the decision-making and implementation processes involved in an advisory programme was performed in ten selected football clubs. Guided interviews were conducted on the basis of the four streams of the garbage can model. The interviews were analysed with qualitative content analysis. Findings Results show that three types of club can be distinguished in terms of their implementation processes: low implementation of the external input; partial implementation of the external input; and rigorous implementation of the external input. In addition, the analysis shows that the participants in the advisory programme are the key actors in both the decision-making process and the implementation. Originality/value The paper provides insights into the practicability of advisory programmes for sport clubs and the transfer to the clubs’ practical decision-making routines. Additionally, it shows how sport clubs deal with (external) advisory impulses, and which different decision-making practices underlie these processes.
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Steen, John, Jerad A. Ford, and Martie-Louise Verreynne. "Symbols, Sublimes, Solutions, and Problems: A Garbage Can Model of Megaprojects." Project Management Journal 48, no. 6 (December 2017): 117–31. http://dx.doi.org/10.1177/875697281704800609.
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In this article, we deploy Cohen, March, and Olsen's (1972) garbage can model of decision making to produce a different lens on the performance of megaprojects. Using a sample of firms involved in hydrocarbon megaprojects, we show that the problems given the most public attention by the industry are different from those responsible for budget overruns. Furthermore, the attribution of reasons for exceeding project budget differs between project owners and supply chain firms. This is consistent with garbage can model predictions around problem latency when the multifaceted symbolism of these projects drives divergent prioritization of problems in project execution.
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Fardal, PhD, Harald, and Ann-Kristin Elstad, PhD. "Decision-making in crisis management of a serious digital incident: A garbage can approach." Journal of Emergency Management 18, no. 6 (November 1, 2020): 489–98. http://dx.doi.org/10.5055/jem.2020.0503.
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Managing crisis challenges the ability to make numerous decisions under great uncertainty. This study address the decision-making process, and how the mix of involved individuals, prior knowledge, and available decision-makers forms the decisions made during a crisis. A large-scale exercise with a cyberattack scenario was chosen as the study’s case. The organization studied have highly skilled crisis management personnel; however, they are not used to manage a large-scale cyber-attack scenario. The garbage can model (GCM) of Organizational Choice with a few modifications is used as the analytical framework in the study.
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Styhre, Alexander, Leena Wikmalm, Sanne Olilla, and Jonas Roth. "Garbage-Can Decision Making and the Accommodation of Uncertainty in New Drug Development Work." Creativity and Innovation Management 19, no. 2 (June 2010): 134–46. http://dx.doi.org/10.1111/j.1467-8691.2010.00551.x.
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Lai, Shih-Kung. "Effects of Planning on the Garbage-Can Decision Processes: A Reformulation and Extension." Environment and Planning B: Planning and Design 30, no. 3 (June 2003): 379–89. http://dx.doi.org/10.1068/b12981.
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A computer simulation, based on the garbage-can decisionmaking process, is presented in order to investigate effects of making plans and of two other organizational characteristics—namely, decision cost and problem disutility—on the behavior of complex systems. In this simulation a three-factor factorial design was implemented where decision cost, problem disutility, and planning investment were considered in terms of combinations of six levels of these factors. The results suggest that all three factors have statistically significant effects on the behavior of the complex system. In particular, decision cost and problem disutility tended to have effects counter to each other, whereas planning resulted in more efficient decisionmaking, but at the cost of resolving fewer problems.
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Lin, Ching Hai, Hsin Chuan Kuo, and Chao Tsung Lee. "An Intelligent Garbage Can Decision-Making Model Evolution Algorithm on Optimal Design of Fuzzy Controller of Intelligent Lighting Systems." Applied Mechanics and Materials 284-287 (January 2013): 2215–19. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2215.
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In this paper, an intelligent garbage can model-based differential evolution algorithm (IGCMDE) is proposed to simulate human social organization by its population system, based on the differential evolution algorithms (DEs) and the logical framework of the garbage can decision model with group meeting. When faced with issues such as unclear goals and technologies, participators turnover, etc., representatives of all participating parties will communicate, argue, compromise and adapt with each other, in order to find a solution to the problems. Group meetings are conducted to choose the best solution in a more objective, reasonable and efficient way. At last, we used IGCMDE to optimize the fuzzy controller with fuzzy logic control theory. We present a method for daylight blending control with two novel contributions: 1) smart luminance sensing; 2) daylight luminance control. The proposed method was implemented as an intelligent lighting system in a parking tower environment. The result demonstrated that IGCMDE possesses an excellent search performance and the intelligent lighting system showed significant energy savings.
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Sheridan, Thomas B. "Deminer Decision Making: Criteria and Models." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 10 (October 1998): 758–62. http://dx.doi.org/10.1177/154193129804201022.
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This paper discusses the application of signal detection theory to improving the efficiency and safety of humanitarian demining. Following an introductory description of the current compelling world-wide needs for improved demining technology, an analytical model is proposed. The model, based on conventional ideal observer or signal detection theory, assumes that one or set of detection instruments yield varying degrees of evidence of a mine at a given location. Given relative costs for missed detections and for false alarms, plus some probability estimates, the model specifies that degree of evidence at which the given location should “ideally” be treated as a live mine and efforts made to remove it or destroy it in place. It is suggested that this normative model can be implemented in a computer chip as a decision aid and be attached to any detection device. This would then assist the user in deciding when to treat a signal as indicative of an actual mine (risking that time will be wasted digging up some metal or plastic that is not a mine), and when to assume that the signal is not a mine (risking that it is in fact a mine which may subsequently be set off and cause injury or death).
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Parent, Milena M. "Decision Making in Major Sport Events Over Time: Parameters, Drivers, and Strategies." Journal of Sport Management 24, no. 3 (May 2010): 291–318. http://dx.doi.org/10.1123/jsm.24.3.291.
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The purpose of this article was to examine how the decision-making process changes as a major sport event’s organizing committee moves from the planning to the implementation to the wrap-up modes. A case study of the 1999 Pan American Games, its organizing committee, and its stakeholders was built by means of interviews and archival material. Velocity impacted decision making in different ways. First, the importance of the time, context, and resources parameters changed, as did the model of decision making (from administrative to garbage can to rational). As well, four drivers of decision making (structural dimensions, stakeholder interactions, information management, and personal characteristics) were found. A key strategy for decision makers faced with an increasing velocity environment was planning for the need to react (come Games time) through risk assessments and contingency plans.
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Kuo, Hsin-Chuan, Jinn-Tong Chiu, and Ching-Hai Lin. "Intelligent Garbage Can Decision-Making Model Evolution Algorithm for optimization of structural topology of plane trusses." Applied Soft Computing 12, no. 9 (September 2012): 2719–27. http://dx.doi.org/10.1016/j.asoc.2012.03.011.
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Trondal, Jarle. "Ambiguities in organizations and the routines of behavior and change." International Journal of Organizational Analysis 23, no. 1 (March 9, 2015): 123–41. http://dx.doi.org/10.1108/ijoa-04-2013-0654.
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Purpose – The aim of this article is threefold: the primary aim is conceptual by outlining two ideal-typical ideas about organizational life. These models offer rival ideas about how organizations balance seemingly conflicting patterns of behaviour and change in everyday life. The second ambition of the article is to outline a theoretical approach of organizational life arguing that even fairly loosely coupled organizations may be profoundly patterned by everyday routines as much as by ambiguity. The third and final ambition is to offer empirical illustrations from organizations that are often considered as archetypes of loose coupling and ambiguities: jazz orchestras and university organizations. The empirical discussion, however, illustrates that behaviour and change in these organizations are coined by routines and rules. Design/methodology/approach – Two common dynamics often observed in organizations are highlighted: first, organizations viewed as sets of formal structures and routines that systematically bias organizational performance and change, and secondly, organizations as loosely coupled structures that enable improvisation with respect to organizational performance and change. How organizations live with and practice such seemingly contradictory dynamics is empirically illuminated in two types of organizations that are seldom analysed in tandem – university organizations and jazz orchestras. Drawing on contemporary research on these seemingly contradictory laboratories of organizational analysis, some observations are highlighted that indeed are common to both types of organizations. Furthermore, it is argued that lessons may be drawn from organizations where turbulence is common and where seemingly un-organized processes are quite regular. University organizations and jazz orchestras represent such types of organizations. Findings – First, the degree of ambiguity in organizations is a matter of degree, not an either/or, and that the uncertainty and spontaneity observed in organizational behaviour and change is more patterned than often assumed (see Heimer and Stinchcombe, 1999; Strauss, 1979). As such, organization theory may be a useful extension of the garbage can model, suggesting that streams in decision-making processes may be systematically pre-packed and patterned by the availability of access and attention structures (Cohen et al., 1976). Secondly, scholarship in organizational studies needs to do away with over-simplistic dichotomies when facing complex realities. This challenge is equal for studies of public sector organizations as for scholarship in business and management. Organization studies often face the tyranny of conceptual dichotomies (Olsen, 2007). This article suggests that the distinction between loose and tight coupling in organizations, as between improvisation and pre-planned activities in organizations, face the danger of shoehorning complex data into simple categories. Originality/value – How organizations live with and practice seemingly contradictory dynamics is empirically illuminated in two types of organizations that are seldom analysed in tandem in organizational studies – university organizations and jazz orchestras. These conflicting organizational dynamics pinpoint one classical dilemma in university and jazz life beleaguered on the inherent trade-off between instrumental design and the logic of hierarchy on the one hand, and individual artistic autonomy and professional neutrality on the other. “[T]he purpose of developing the jazz metaphor is to draw out the collaborative, spontaneous and artful aspects of organizing in contradiction to the engineered, planned and controlled models that dominate modern management thoughts” (Hatch, 1999, p. 4). This dilemma highlights competing understandings of organizational life, of institutional change, and of what the pursuit of organizational goals ultimately entails.
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Bloom, Laura A., and Bernard S. Bloom. "DECISION ANALYTIC MODELING IN HEALTH CARE DECISION MAKING." International Journal of Technology Assessment in Health Care 15, no. 2 (May 1999): 332–39. http://dx.doi.org/10.1017/s0266462399015251.
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The need to choose among alternatives instead of allowing the market to make choices has led health care professionals to rely on scientific information as an aid in decision making. Mathematical modeling is one of the increasingly common tools used over the past three decades to produce new information. But we have used almost exclusively noncomplex models to help analyze complex systems problems. The need to integrate the complexity of the interactions of clinical, quality of life, and economic attributes into such models can no longer be ignored. The opportunity is available to use existing complex systems modeling techniques for health care questions to improve the quality of study outputs, which can, in turn, help produce more rational decisions.
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YU, Xiaohan, Zeshui XU, and Shousheng LIU. "SYSTEMATIC DECISION MAKING: A EXTENDED MULTI-CRITERIA DECISION MAKING MODEL." Technological and Economic Development of Economy 23, no. 1 (January 22, 2017): 157–77. http://dx.doi.org/10.3846/20294913.2016.1212121.
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Considering some complex multi-criteria decision making (MCDM) problems, in which decision environment is dynamic, there are various interdependences among criteria, and plans (systematized alternatives) consisting of multiple time sequential interdependent actions, cannot be well handled by means of the existing MCDM methods, therefore, we develop a systematic decision making (SDM) as an improvement and supplement of the classic MCDM in this paper. The SDM is for prescribing methods of evaluating and selecting the most favourite plan (a system) from a group of feasible ones concerning influences of time-varying criteria system under dynamic external environment. Through detailed analysis, we separate a SDM problem into multi-period MCDM subproblems, and then a plan can be a combination of time sequential strategies in which each strategy (a subset of actions) is a feasible decision choice of corresponding MCDM sub-problem. After clarifying variety of interdependences, interactions and interrelationships in the SDM problems, such as criteria-interdependences, action-interdependences, interactions between criteria and criteria system, interactions between actions and strategies, interactions between strategies and plans, interactions between internal environment (criteria system) and external environment, feedbacks from external environment to the corresponding MCDM sub-problems, and interrelationships among MCDM sub-problems and so on, we transform the SDM into multi-period interrelated MCDM model which can be dealt with more easily by using multiple optimization models. At the end of the paper, three typical properties of the SDM are proposed and most of the existing MCDM models are pointed out as special cases of the SDM.
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Azadfallah, Mohammad. "Multi-Criteria Decision Making for Ranking Decision Making Units." International Journal of Productivity Management and Assessment Technologies 6, no. 1 (January 2018): 17–36. http://dx.doi.org/10.4018/ijpmat.2018010102.
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There is no doubt the Data Envelopment Analysis (DEA) is a powerful method for the efficiency evaluation of Decision Making Units (DMUs) with multiple inputs and outputs. Despite its usefulness, DEA has some notable limitations. A significant drawback with this approach is that inability to fully rank the DMUs. In the extant literature, different methods for this purpose have been suggested. While, in the traditional method the first step for the DEA approach is used, and results of this step are input for the DEA ranking method in the second step. To reduce the computational complexity of the traditional method, a new Multiple Criteria Decision Making (MCDM) approach is proposed in this article. In the proposed approaches, one step can achieve full ranking for all DMUs. The results show that although out of 20 DMUs are first in the final ranking ordered by the DEA, the author proposed methods can consider full ranking. Agreement of the proposed methods with the existing approaches are measured by the Spearman's rank correlation coefficient technique. The findings of this study reveal that TOPSIS, Neo-TOPSIS, and AHP ranking results are consistent with the DEA ranking method. Therefore, these proposed methods appear as the possible alternatives to the DEA and DEA ranking models.
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Li, Liu, and Wei. "A Dynamic Decision Making Method Based on GM(1,1) Model with Pythagorean Fuzzy Numbers for Selecting Waste Disposal Enterprises." Sustainability 11, no. 20 (October 9, 2019): 5557. http://dx.doi.org/10.3390/su11205557.
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With the rapid development of society and the economy, most cities have to face a serious problem of “Garbage Siege”. The garbage classification is imperative because the traditional disposal method for household solid waste is not suitable for this situation. The Chinese government proposed a public private partnership (PPP) style to increase the efficiency of garbage disposal in 2013. An effective method to evaluate the waste disposal enterprises is essential to choose suitable ones. A reasonable evaluation method should consider enterprises’ performance not only now but also in the future. This paper aims to propose a dynamic decision making method to evaluate the enterprises’ performance based on a GM(1,1) model and regret theory with Pythagorean fuzzy numbers (PFNs). First, we proposed a GM(1,1) model for predicting score function of PFNs. Then, we put forward a method to obtain the prediction of grey degree using OWA operator. Based on the prediction of score function and grey degree, we established a novel GM(1,1) model of PFNs. Furthermore, we utilized the grey incidence method to obtain the criteria weights with Pythagorean fuzzy information. We used the regret theory to aggregate information and rank the alternatives. Finally, we applied our proposed method to solve the selecting waste disposal enterprises problem in Shanghai. By the case study we can obtain that our method is effective to solve this problem.
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Jankowska, Joanna. "Psychological Insights Into Decision‑Making." Kwartalnik Ekonomistów i Menedżerów 42, no. 4 (October 1, 2016): 0. http://dx.doi.org/10.5604/01.3001.0009.5485.
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This paper considers the widely approached problem of how individuals and groups make economic decisions. The author’s belief is that the answer to this question is highly interdisciplinary and lies not only in areas of study such as microeconomic theory and organisational behaviour, but also psychology, neuroscience and ethics. The author attempts to summarise a few chosen, existing models, which can help analyse both logical and psychological aspects of the process, and mentions a new, rising interdisciplinary field of neuroeconomics, which offers high potential for construction of new decision‐making models in the future.
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Bell, Steven J., and Cynthia Cronin-Kardon. "Making the Library Management Systems Acquisition: Achieving Resolution of a Tough Decision." College & Research Libraries 59, no. 4 (July 1, 1998): 347–60. http://dx.doi.org/10.5860/crl.59.4.347.
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At some point in their careers, library administrators will likely be involved in the acquisition of a new library automation system. Whether it is a first-time acquisition or a migration from old to new, the decision is perhaps the most challenging the administrator will ever have to make. Despite an abundance of information in the library literature on the mechanical and managerial aspects of acquiring a new automated system, there is scant information on, or investigation into, the decision-making process that leads to the selection of an automation vendor. Based on the premise that the automation decision is both complex and risky, it is a decision fraught with ambiguity, uncertainty, and conflict. This study offers exploratory research on the automation decision process and those factors that lead to decision difficulty. Using the Garbage Can Model of Organizational Choice as a theoretical framework, this study uses decision resolution as a criterion of decision success. Based on research to identify factors that have enabled decision makers to achieve resolution, the authors of this study seek to make recommendations that will enable administrators to better manage a tough decision.
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Chan, Chien-Lung, and Chi-Chang Chang. "Big Data, Decision Models, and Public Health." International Journal of Environmental Research and Public Health 17, no. 18 (September 15, 2020): 6723. http://dx.doi.org/10.3390/ijerph17186723.
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Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.
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Naplyokov, Yuriy Vasilievich. "CHANGING OF MENTAL MODELS FOR EFFECTIVE DECISION-MAKING." UKRAINIAN ASSEMBLY OF DOCTORS OF SCIENCES IN PUBLIC ADMINISTRATION 1, no. 11 (January 24, 2018): 209–28. http://dx.doi.org/10.31618/vadnd.v1i11.28.
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This article explains the role of mental models and the need of their change to make effective decisions. It is substantiated that the mental model rests on changes to save the system and minimize the risk. An example of this resis- tance is the complicated and slow process of political reform in Ukraine, which forms a new national mentality model. Political initiatives are aimed at creating a new legitimate mental model, which should be more effective than the previ- ous, in a new environment. But from 2014 to 2017 of the nearly five thousand proposed legislative proposals, the Verkhovna Rada of Ukraine has only adopted a few dozen. It is noted that the review of mental models is a complex process that requires additional energy expenditure, such as stress, loss of comfort, security, money, etc. The ability to change the mental model may require personal courage, creativity, independence, and imagination. To view mental models, the leader must apply the appropriate leadership power and styles, establish an appropriate organizational culture and climate, show positive and optimistic behavior to en- courage team members and motivate them to change.It is noted that in the new environment, the decision maker can fluctuate closely to the so-called “line of comfort” for making a decision. This is a line of psychological comfort according to the existing mental model. For better and faster decision-making, you may need to create a new “line of comfort” by looking at the mental model. Thus, in a new environment, the decision maker can again make decisions on the basis of a new mental model.It is proved that mental models are relatively stable, but changing the envi- ronment makes them look. The growing conflict between the system and the en- vironment inevitably forms a new mental model, which should again balance the system.
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Naplyokov, Yuriy Vasilievich. "CHANGING OF MENTAL MODELS FOR EFFECTIVE DECISION-MAKING." UKRAINIAN ASSEMBLY OF DOCTORS OF SCIENCES IN PUBLIC ADMINISTRATION 1, no. 12 (February 14, 2018): 209–28. http://dx.doi.org/10.31618/vadnd.v1i12.63.
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This article explains the role of mental models and the need of their change to make effective decisions. It is substantiated that the mental model rests on changes to save the system and minimize the risk. An example of this resistance is the complicated and slow process of political reform in Ukraine, which forms a new national mentality model. Political initiatives are aimed at creating a new legitimate mental model, which should be more effective than the previous, in a new environment. But from 2014 to 2017 of the nearly five thousand proposed legislative proposals, the Verkhovna Rada of Ukraine has only adopted a few dozen. It is noted that the review of mental models is a complex process that requires additional energy expenditure, such as stress, loss of comfort, security, money, etc. The ability to change the mental model may require personal courage, creativity, independence, and imagination. To view mental models, the leader must apply the appropriate leadership power and styles, establish an appropriate organizational culture and climate, show positive and optimistic behavior to encourage team members and motivate them to change. It is noted that in the new environment, the decision maker can fluctuate closely to the so-called “line of comfort” for making a decision. This is a line of psychological comfort according to the existing mental model. For better and faster decision-making, you may need to create a new “line of comfort” by looking at the mental model. Thus, in a new environment, the decision maker can again make decisions on the basis of a new mental model. It is proved that mental models are relatively stable, but changing the environment makes them look. The growing conflict between the system and the environment inevitably forms a new mental model, which should again balance the system.
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Fuglseth, Anna Mette, and Kjell Grønhaug. "Can computerised market models improve strategic decision-making? An exploratory study." Journal of Socio-Economics 32, no. 5 (November 2003): 503–20. http://dx.doi.org/10.1016/j.socec.2003.08.007.
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Ben Assuli, Ofir. "Implementing combined decision models in healthcare settings: the Simon and Pauker- Kassirer models." Journal of Hospital Administration 3, no. 2 (October 22, 2013): 10. http://dx.doi.org/10.5430/jha.v3n2p10.
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Background: Modeling medical decision-making has attracted considerable attention over the years, and has become the topic of many investigations. Researchers have attempted to model this critical and extremely complex process from several different angles to enable hospital clinicians to engage in decision-making using empirical tools. Purpose: This paper takes a famous managerial model of decision-making in a non-medical setting and integrates it with a well- known model of medical decision-making to generate a unified illustration of the process. Both models deal with decision-making. However, Simon’s model is less easily applied to the unique process of medical decision making. The proposed integration may help bridge the gap between the models and approaches by creating a unified framework to deal with the challenge of medical decision making in hospital environments through empirical methods. Approach: Simon’s model of automation provides the general structure of the decision-making process by dividing it into three stages: Intelligence, Design and Choice. The Pauker & Kassirer model deals with probabilistic and statistical applications of clinical processes, and introduces a threshold approach and decision trees as the main decision tools. The discussion explores the advantages and disadvantages of each model and what can be gained by combining them. Research limitations: Although these models were used to form an integrated framework, they were developed almost three decades apart. Therefore, caution is of the essence when applying them to real-life circumstances, and further research is needed to validate this integration.
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Hayes, Sherman L., and Patricia B. McGee. "“Garbage can decision making” in a “structured anarchy” for your CWIS. Could you translate that for me please?" Campus-Wide Information Systems 15, no. 1 (March 1998): 29–33. http://dx.doi.org/10.1108/10650749810369894.
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Kujawski, Edouard, Evangelos Triantaphyllou, and Juri Yanase. "Additive Multicriteria Decision Analysis Models: Misleading Aids for Life-Critical Shared Decision Making." Medical Decision Making 39, no. 4 (May 2019): 437–49. http://dx.doi.org/10.1177/0272989x19844740.
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Background. There is growing interest in multicriteria decision analysis (MCDA) for shared decision making (SDM). A distinguishing feature is that a preferred treatment should extend years of life and/or improve health-related quality of life (HRQL). Additive MCDA models are inadequate for the task. A plethora of MCDA models exist, each claiming that it can correctly solve real-world problems. However, most were developed in nonhealth fields and rely on additive models. This makes the problem of choosing an MCDA model as an aid for SDM a challenging and urgent one. Methods. A published 2017 MCDA of a hypothetical prostate cancer patient is used as a case in point of how not to do and how to do MCDA for SDM. We critically review it and analyze it using several additive linear MCDA models with years of life and HRQL as attributes and the linear quality-adjusted life-year (QALY) model. The following simple reasonableness test is presented for applicability of a method as an aid for SDM: Can a treatment that causes premature death trump a treatment that causes acceptable adverse effects? Results. Additive MCDA models and the linear QALY recommend significantly different alternatives. Additive MCDA models fail the proposed reasonableness test; the linear QALY model passes. Conclusions. MCDA possesses a strong craft element in addition to its technical aspects. MCDA practitioners and clinicians need to understand model limitations to choose models appropriate to the context. Additive MCDA models are inadequate for life-critical SDM. We advocate QALY models with additional research for increased realism as a tool for SDM.
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Atsan, Nuray. "Decision-Making under Stress and Its Implications for Managerial Decision-Making: A Review of Literature." International Journal of Business and Social Research 6, no. 3 (April 9, 2016): 38. http://dx.doi.org/10.18533/ijbsr.v6i3.936.
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<p>We examine the main theoretical models of decision making under stress and the effects of decision stress on decision making process to provide a deeper understanding of the decision making phenomenon. The literature review reveals that stress can have an impact on each stage of the decision-making process. The review also finds that decision makers could enhance their decision-making performance and prevent potential decision failures by means of adapting certain coping strategies.</p>
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Crockett, Molly J. "How Formal Models Can Illuminate Mechanisms of Moral Judgment and Decision Making." Current Directions in Psychological Science 25, no. 2 (April 2016): 85–90. http://dx.doi.org/10.1177/0963721415624012.
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Levitt, Barbara, and Clifford Nass. "The Lid on the Garbage Can: Institutional Constraints on Decision Making in the Technical Core of College-Text Publishers." Administrative Science Quarterly 34, no. 2 (June 1989): 190. http://dx.doi.org/10.2307/2989895.
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XU, ZESHUI, and HUI HU. "PROJECTION MODELS FOR INTUITIONISTIC FUZZY MULTIPLE ATTRIBUTE DECISION MAKING." International Journal of Information Technology & Decision Making 09, no. 02 (March 2010): 267–80. http://dx.doi.org/10.1142/s0219622010003816.
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The aim of this paper is to investigate the intuitionistic fuzzy multiple attribute decision-making problems where the attribute values are expressed in intuitionistic fuzzy numbers or interval-valued intuitionistic fuzzy numbers. We introduce some notions, such as intuitionistic fuzzy ideal point, interval-valued intuitionistic fuzzy ideal point, the modules of intuitionistic fuzzy numbers, and interval-valued intuitionistic fuzzy numbers. We also introduce the cosine of the included angle between the attribute value vectors of each alternative and the intuitionistic fuzzy ideal point, and the cosine of the included angle between the attribute value vectors of each alternative and the interval-valued intuitionistic fuzzy ideal point. Then we establish two projection models to measure the similarity degrees between each alternative and the intuitionistic fuzzy ideal point, and between each alternative and the interval-valued intuitionistic fuzzy ideal point. Based on the projection models, we can rank the given alternatives and then select the most desirable one. Finally, we illustrate the developed projection models with a numerical example.
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Pothos, Emmanuel M., Oliver J. Waddup, Prince Kouassi, and James M. Yearsley. "What Is Rational and Irrational in Human Decision Making." Quantum Reports 3, no. 1 (March 19, 2021): 242–52. http://dx.doi.org/10.3390/quantum3010014.
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There has been a growing trend to develop cognitive models based on the mathematics of quantum theory. A common theme in the motivation of such models has been findings which apparently challenge the applicability of classical formalisms, specifically ones based on classical probability theory. Classical probability theory has had a singularly important place in cognitive theory, because of its (in general) descriptive success but, more importantly, because in decision situations with low, equivalent stakes it offers a multiply justified normative standard. Quantum cognitive models have had a degree of descriptive success and proponents of such models have argued that they reveal new intuitions or insights regarding decisions in uncertain situations. However, can quantum cognitive models further benefit from normative justifications analogous to those for classical probability models? If the answer is yes, how can we determine the rational status of a decision, which may be consistent with quantum theory, but inconsistent with classical probability theory? In this paper, we review the proposal from Pothos, Busemeyer, Shiffrin, and Yearsley (2017), that quantum decision models benefit from normative justification based on the Dutch Book Theorem, in exactly the same way as models based on classical probability theory.
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Yan, Fei Xue, Jing Xia, Guan Qun Shen, and Xu Sheng Kang. "A Crime Decision-Making Model Based on AHP." Applied Mechanics and Materials 50-51 (February 2011): 885–89. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.885.
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As time goes by, hazard rate of the society would increase if crime prediction was not implemented. Based on objective factors of offenders and victims characteristics, AHP method can be established to get a quantitative and qualitative analysis on crime prediction. Crime prediction is a strategic and tactical measure for crime prevention. According to AHP analysis, two prediction models of the optimal predictive crime locations are put forward. Standard Deviational Ellipses Model and Key Feature adjusted Spatial Choice Model were formulated to account for the anticipated position with various elements from AHP method. These models could be applied in a computer simulation of situation tests of the series murders. Besides, applying those models in certain real case demonstrates how the models work. Through models comparison, the results are summarized that Key Feature adjusted Spatial Choice Model is more conducive in confirming the guilty place. In conclusion, the suggested models, including detailed criminal map, are easy to implement.
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Dogandžić, Aleksandar, and Sonja Dogandžić. "Behavioral models of irrational decision-making in the financial market." Ekonomski pogledi 22, no. 1 (2020): 79–92. http://dx.doi.org/10.5937/ekopog2001079d.
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The decision-making process within financial management requires continuous study and analysis. Classical theory is based on the decision-making process on the hypothesis of market efficiency on rational decision-making that brings effects that can be estimated by the law of probability. Rationality in the decision-making process is always classified as expected behavior. However, in practice, human behavior is not always rational and that is why it is the subject of continuous research. It means getting acquainted with human behavior, what they do, why they do something and what are the consequences of their work. In that sense, in relation to financial management and the decision-making process, a special discipline stands out, behavioral finance, which deals with the study of this process, irrationality in decision-making or the study of inadequate use of all relevant data that could affect rational decision-making.
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Jere Jakulin, Tadeja. "System dynamics models as decision-making tools in agritourism." Agricultura 13, no. 1-2 (December 1, 2016): 5–10. http://dx.doi.org/10.1515/agricultura-2017-0002.
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Abstract Agritourism as a type of niche tourism is a complex and softly defined phaenomenon. The demands for fast and integrated decision regarding agritourism and its interconnections with environment, economy (investments, traffic) and social factors (tourists) is urgent. Many different methodologies and methods master softly structured questions and dilemmas with global and local properties. Here we present methods of systems thinking and system dynamics, which were first brought into force in the educational and training area in the form of different computer simulations and later as tools for decision-making and organisational re-engineering. We develop system dynamics models in order to present accuracy of methodology. These models are essentially simple and can serve only as describers of the activity of basic mutual influences among variables. We will pay the attention to the methodology for parameter model values determination and the so-called mental model. This one is the basis of causal connections among model variables. At the end, we restore a connection between qualitative and quantitative models in frame of system dynamics.
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Murray, PhD, PE, Susan L., Kashmeera Ghosh, MS, and Mala Gosakan, MS. "Human performance modeling for emergency management decision making." Journal of Emergency Management 8, no. 5 (September 1, 2010): 17. http://dx.doi.org/10.5055/jem.2010.0029.
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Objective: Computer simulation models allow users to analyze problems and identify improvements. Human performance models (HPMs) are a type of computer simulation model that is used to study and evaluate complex operations involving humans completing tasks. This article describes the advantages that HPMs can have for those involved in emergency management.Design: IMPRINT Pro is an HPM software tool developed by the US Army Research Laboratory. It is a stochastic discrete-event network modeling tool. The modeling process includes defining tasks to be completed, the personnel responsible for performing the task, the success probability for each task and the operation as a whole, resource availability and limitations, and other features to evaluate scenarios. The results include easy-to-use task network diagrams and corresponding performance metrics. The models can be used as a preplanning and training tool to improve an organization’s performance.Setting: To demonstrate the benefits of simulation modeling for emergency management, a case study of a combined anthrax and bomb threat made at a university is presented. Data from first responders including police and fire departments and the procedures used are modeled.Results: The case study shows the complexity of many emergency management situations. Human performance modeling is a powerful tool that can provide insight to different possibilities in these complex situations and can predict outcomes without having to go through an actual emergency event or costly drills. Computer modeling saves money, time, and efforts for emergency managers and responders. These models serve as useful training and evaluation tools.
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Galli, Brian J. "Economic-Decision-Making in New Product Development." International Journal of Applied Management Sciences and Engineering 7, no. 1 (January 2020): 1–27. http://dx.doi.org/10.4018/ijamse.2020010101.
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Economic decisions for a new product can impact any subsequent development, as well as the launching of the product. Furthermore, unsuccessful decision-making can result in missing business opportunities or spending more money on rework. This article investigates economic decision-making in the product development process. It also enhances the understanding of the process, the difficulties involved, and how to improve decisions during new product development. Thus, this study can serve as a reference when support methods for economic decisions are being initiated. Industrial engineers and engineering managers use economic decision-making for new product development. The results of this study indicate that economic decisions are vital to new product development, as they also bring radical changes in the fields of IE/EM/PM. The engineering management practitioner will understand the importance of these topics, their relevance to engineering management, and how engineering managers can integrate these ideas into their operations and project management lifecycle and project management settings. Economic decision-making models in IE/EM/PM should replace traditional non-scientific methods because they are inaccurate and speculative at best. Overall, by using the economic models, the engineering manager is prioritizing on the long-term prospect that the decision will have.
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Chornous, Galyna O. "PROACTIVE DECISION-MAKING MECHANISM BASED ON MINING TECHNOLOGY." Ekonomika 91, no. 1 (January 1, 2012): 105–17. http://dx.doi.org/10.15388/ekon.2012.0.904.
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The main idea of this study is to connect the possibilities of mining technology with the methodology of proactive management by social and economic systems. The permanent process of complication of all spheres of social life requires improving the management forms and methods. Modern methods of decision support, appropriate information technology make it possible to improve the classical approaches, one of which is proactive management. Taking into account the limits of classical methods, proactive management should be chosen as an appropriate mining technology that can automatically extract the new non-trivial knowledge from data in the form of patterns, relationships, laws, etc. This synthetic technology combines the latest achievements of artificial intelligence, mathematics, statistics, heuristic approaches, including Data Mining, OLAP and others. Using the mining technology enables: to implement data monitoring, preparation and analysis (collection and presentation of data, detection of situations), to identify problem situations (to recognize patterns of problem situations; to correlate the pattern of the current situation with patterns of problem situations; to determine the structure of the problem situation, to identify factors and relationships), to prioritize the problems, trends and challenges, their expectations, effects (to predict the situation development with managerial influence and without it), to pose the tasks (to analyze deviations in terms of activity; to define goals, criteria, operating conditions) and so on. The following models (using the methods of “nearest neighbour”, rules induction, causal networks, statistical methods, associations, neural networks, decision trees, etc.) can be used: cluster allocation situations, classification of patterns, models of situation identification, pattern recognition models, prediction models, optimization models, causal relationships models.
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Massad, Naji, and Jørgen Andersen. "Three Different Ways Synchronization Can Cause Contagion in Financial Markets." Risks 6, no. 4 (September 21, 2018): 104. http://dx.doi.org/10.3390/risks6040104.
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We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, synchronize in frequency. The integrate-and-fire dynamics happens due to “change blindness”, a trait in human decision-making where people have the tendency to ignore small changes, but take action when a large change happens. The second pathway happens due to feedback mechanisms between market performance and the use of certain (decoupled) trading strategies. The third pathway occurs through the effects of communication and its impact on human decision-making. A model is introduced in which financial market performance has an impact on decision-making through communication between people. Conversely, the sentiment created via communication has an impact on financial market performance. The methodologies used are: agent based modeling, models of integrate-and-fire oscillators, and communication models of human decision-making.
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Boyle, Iain M., Alex H. B. Duffy, R. Ian Whitfield, and Shaofeng Liu. "The impact of resources on decision making." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26, no. 4 (November 2012): 407–23. http://dx.doi.org/10.1017/s0890060412000273.
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AbstractDecision making is a significant activity within industry and although much attention has been paid to the manner in which goals impact on how decision making is executed, there has been less focus on the impact decision making resources can have. This article describes an experiment that sought to provide greater insight into the impact that resources can have on how decision making is executed. Investigated variables included the experience levels of decision makers and the quality and availability of information resources. The experiment provided insights into the variety of impacts that resources can have upon decision making, manifested through the evolution of the approaches, methods, and processes used within it. The findings illustrated that there could be an impact on the decision-making process but not on the method or approach, the method and process but not the approach, or the approach, method, and process. In addition, resources were observed to have multiple impacts, which can emerge in different timescales. Given these findings, research is suggested into the development of resource-impact models that would describe the relationships existing between the decision-making activity and resources, together with the development of techniques for reasoning using these models. This would enhance the development of systems that could offer improved levels of decision support through managing the impact of resources on decision making.
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Stan, Anca. "GROUPS DECISION MAKING WITHIN THE ORGANIZATION." STUDIES AND SCIENTIFIC RESEARCHES. ECONOMICS EDITION, no. 13 (December 17, 2008): 94. http://dx.doi.org/10.29358/sceco.v0i13.29.
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In a highly global market, organizations that have the ability to analyze and rapidly respond to the constantly changing marketplace will have the greatest chance of remaining competitive and profitable. Group decision making is the process of arriving at a judgment based upon the feedback of multiple individuals. Due to the importance of the group decision making process, decision making models can be used to establish a systematic means of developing effective group decision making. Once a decision has been made, the members of the group should be willing to accept it and support its implementations.
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G. Marimuthu, and G. Ramesh. "Comparison among Original AHP, Ideal AHP and Moderate AHP Models." International Research Journal of Engineering, IT & Scientific Research 2, no. 5 (May 1, 2016): 40. http://dx.doi.org/10.21744/irjeis.v2i5.26.
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Decisions always involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques. Every decision making can be considered as the choice from the set of alternatives based on a set of criteria. The analytic hierarchy process (AHP) is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons. This paper is concerned with the moderate AHP decision model is always same as the original AHP decision model. It does not violate the rule itself.
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Seidenstücker, Gerhard, and Wolfgang L. Roth. "Treatment Decisions: Types, Models and Schools." European Journal of Psychological Assessment 14, no. 1 (January 1998): 2–13. http://dx.doi.org/10.1027/1015-5759.14.1.2.
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Psychotherapy can be seen as a complex decision process. First, we clarify some distinctions that have to be made in order to adequately describe and evaluate the state of affairs. Then we illustrate decisions made by clients, semi-professionals, and professionals, and mention descriptive and prescriptive studies in treatment-related decision-making. Next, we focus on questions confronting therapists before and during psychotherapy, i. e., the selection, design, and implementation of an efficient therapy offer. These questions motivate the definition of different pragmatic indication models - the selective, inventive and adaptive models. In the next to last section we describe and comment on assessment strategies and decision rules in the context of therapeutic schools (psychoanalysis, client-centered psychotherapy, cognitive-behavioral psychotherapy). In the last section we outline contributions towards general models of decision making in psychological treatments and formulate some conclusions for further research in clinical decision-making.
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Pirrone, Angelo, and Fernand Gobet. "Modeling Value-Based Decision-Making Policies Using Genetic Programming." Swiss Journal of Psychology 79, no. 3-4 (December 2020): 113–21. http://dx.doi.org/10.1024/1421-0185/a000241.
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Abstract. An important way to develop models in psychology and cognitive science is to express them as computer programs. However, computational modeling is not an easy task. To address this issue, some have proposed using artificial-intelligence (AI) techniques, such as genetic programming (GP) to semiautomatically generate models. In this paper, we establish whether models used to generate data can be recovered when GP evolves models accounting for such data. As an example, we use an experiment from decision-making which addresses a central question in decision-making research, namely, to understand what strategy, or “policy,” agents adopt in order to make a choice. In decision-making, this often means understanding the policy that best explains the distribution of choices and/or reaction times of two-alternative forced-choice decisions. We generated data from three models using different psychologically plausible policies and then evaluated the ability and extent of GP to correctly identify the true generating model among the class of virtually infinite candidate models. Our results show that, regardless of the complexity of the policy, GP can correctly identify the true generating process. Given these results, we discuss implications for cognitive science research and computational scientific discovery as well as possible future applications.