Taking stock of behavioural OR: A review of behavioural studies with an intervention focus

Franco, L. Alberto; Hämäläinen, Raimo P.; Rouwette, E.A.J.A.; Leppänen, Ilkka

doi:10.1016/j.ejor.2020.11.031

Cited by 44 publications

(19 citation statements)

References 148 publications

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“…Traditionally, decision support models have been built on the assumption of rational decision making in which preferences among the alternatives are only contingent on how these alternatives perform with regard to prespecified decision objectives. However, a recent trend started to look at behavioral factors that affect decision making (see, e.g., Morton and Fasolo 2009, Hämäläinen et al 2013, Franco and Hämäläinen 2016, Franco et al 2021. Decision support models in multi-objective decision contexts can also be subject to behavioral effects introduced by reference points (Korhonen et al 1990, Tversky andKahneman 1991), as well as other cognitive and psychological factors (Evans and Stannovich 2013, Katsikopoulos 2014, Montibeller and von Winterfeldt 2015.…”

Section: Behavioral Aspects In Multi-objective Decision Makingmentioning

confidence: 99%

On rational behavior in multi-attribute riskless choice

Korhonen

Wallenius

Genç

et al. 2021

European Journal of Operational Research

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Section: Behavioral Aspects In Multi-objective Decision Makingmentioning

confidence: 99%

On rational behavior in multi-attribute riskless choice

Korhonen

Wallenius

Genç

et al. 2021

European Journal of Operational Research

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“…The effects of model-driven technologies have been documented in case studies and several reviews have appeared (Mingers, 2000;Mingers & Rosenhead, 2004;Rouwette, 2016;Rouwette et al, 2002;Scott et al, 2015). More recently, Franco, Hämäläinen, Rouwette, and Leppänen (2020) analyze the effects of modeldriven technologies on behavior as part of a review of the impacts of interventions using Operational Research approaches.…”

Section: Research Focus and Major Theoriesmentioning

confidence: 99%

“…Although there also seems to be a range of theories (as can be seen from Table 2), further exploration is likely to show similarities with regard to proposed relations between concrete variables, and that differences between theories stem from different dependent variables or contexts for which theories were developed. For instance, a recent review of behavioral studies with a focus on decision support in the Operational Research field (Franco et al, 2020) categorizes studies along two dimensions: a deterministic versus voluntarist view on behavior (depending on whether participant behavior is exogenously driven or have agency in how to employ a particular intervention), and a variance versus process methodology (depending on whether causal relations between intervention characteristics and outcomes are studied, or a temporal perspective is taken). Depending on the theories relevant to a particular setting and outcome variable, relevant intervention characteristics can be identified.…”

Section: Researchmentioning

confidence: 99%

Technologies for Improving Group Decision making

Rouwette¹,

Franco²

2021

The Emerald Handbook of Group and Team Communication Research

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This chapter focuses on techniques and technologies to aid groups in making decisions, with an emphasis on computer-based support. It is now customary for many office workers to regularly meet colleagues and clients in virtual meetings using videoconferencing platforms, which enable participants to carry out tasks in a manner quite similar to a face-to-face meeting. The development of computer-based platforms to facilitate group tasks can be traced back to the 1960s, and while they support group communication, they do not directly support group decision making. In this chapter we distinguish four technologies developed to provide support to group decisions, clustered into two main traditions. Technologies in the taskoriented tradition are mainly concerned with enabling participants to complete tasks to solve the group's decision problem via computer-supported communications. Group Decision Support Systems and social software technologies comprise the task-oriented tradition.Alternately, in the model-driven tradition, participants use computers to build and use a model that acts as a referent to communicate, mostly verbally, about the group's decision problem. Systems modeling and decision modeling technologies constitute the model-driven tradition.This chapter sketches the history and guiding ideas of both traditions, and describes their associated technologies. Our discussion centers on whether increased availability of online tools will also lead to increased use of group decision support technologies, and the differential impact of communication and decision support.terms technology, technique and tool in specific ways. Technology will refer to a set of techniques and tools plus the guidelines, principles, or assumptions needed to implement and use those. Technique will refer to a set of well-described activities (e.g. Nominal Group Technique). Finally, tool will refer to an artefact (e.g., computer software) that can assist in the deployment of a technique.An influential characterization of levels of group decision-making support is offered by DeSanctis and Gallupe (1987), and may serve to give a first overview of the types of technologies, tecniques and tools available. Level 1 decision support removes communication barriers by offering technical features such as electronic messaging, anonymous gathering, display of ideas on a shared screen, and anonymous clustering and voting. Level 1 tools are similar to videoconferencing in that they facilitate information exhange, but they can also include additional features that directly support decision making, such as anonymous gathering, clustering, and prioritization of ideas. By more fully capturing elements of the discussion, a group memory is created that participants can refer to during and after the meeting. Level 2 decision support adds group decision techniques (e.g., Nominal Group Technique, Delphi). Level 2 tools include decision models to support techniques such as Multi-Criteria Decision Analysis. Level 3 decision support employs machine-induced group co...

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“…Researchers in management and economics have already recognized that the assumptions included in some of the models employed in these fields are somewhat idealized and often do not reflect the characteristics of real-world decisionmakers (Kohn, 2004;Axtell, 2007). While, of course, these models are technically correct and valid, due to the assumptions they build on, they may lack the power to explain empirical phenomena (Franco et al, 2020;Leitner & Behrens, 2015;Roberts & Ng, 2012;Lambright, 2009). Axtell (2007) refers to the core of the idealized assumptions as the neoclassical sweet spot, which includes rationality, agent homogeneity, equilibrium, and non-interactiveness.…”

Section: Introductionmentioning

confidence: 99%

Micro-level dynamics in hidden action situations with limited information

Leitner¹,

Wall²

2021

Preprint

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The hidden-action model provides an optimal sharing rule for situations in which a principal assigns a task to an agent who makes an effort to carry out the task assigned to him. However, the principal can only observe the task outcome but not the agent's actual action. The hidden-action model builds on somewhat idealized assumptions about the principal's and the agent's capabilities related to information access. We propose an agent-based model that relaxes some of these assumptions. Our analysis lays particular focus on the micro-level dynamics triggered by limited information access. For the principal's sphere, we identify the so-called Sisyphus effect that explains why the optimal sharing rule can generally not be achieved if the information is limited, and we identify factors that moderate this effect. In addition, we analyze the behavioral dynamics in the agent's sphere. We show that the agent might make even more of an effort than optimal under unlimited information, which we refer to as excess effort. Interestingly, the principal can control the probability of making an excess effort via the incentive mechanism. However, how much excess effort the agent finally makes is out of the principal's direct control.

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Taking stock of behavioural OR: A review of behavioural studies with an intervention focus

Cited by 44 publications

References 148 publications

On rational behavior in multi-attribute riskless choice

On rational behavior in multi-attribute riskless choice

Technologies for Improving Group Decision making

Micro-level dynamics in hidden action situations with limited information

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