scholarly journals Longshot or Favorite: The Ending Effect in Investment Decisions

2021 ◽  
Vol 12 ◽  
Author(s):  
Qi Wang ◽  
JiaYuan Zhang ◽  
Cai Xing
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Risk Taking ◽  
Past Research ◽  
Psychological Consequences ◽  
Risky Decision ◽  
Risk Levels ◽  
Safe Option ◽  
Complex Decision Making ◽  
Complex Decision

The ending effect describes the phenomenon that at the end of a series of repeated risky decision-making tasks, participants become more likely to engage in risk-taking behavior. Past research has suggested that the ending effect might be caused by a motivational shift induced by changes in time perception. Previous studies mainly tested this phenomenon in a binary decision-making setting (e.g., a decision-making task usually includes two alternatives). However, none of these prior studies included safe options and risky options that differed in risk levels. To address this knowledge gap, the present study replicated the ending effect in a repeated decision-making task that included both a safe option and risky options that differed in risk levels (N = 104). We found that at the end of the decision-making task, participants became more likely to engage in risk-taking and to favor the option with the highest risk. Further, we found that the investment likelihood and investment amount of high-risk options both increased significantly at the ending. In addition, a shift in favoring the safe option emerged in the noninformed condition at the end. We also found that the emotional motivation in the last round could predict the increased preference for high-risk at the ending. This study extended previous findings on the ending effect by adopting a more complex decision-making scenario and, more broadly, helped further our understanding of the psychological consequences of perceived endings.

scholarly journals Supporting surgeons in patient-centred complex decision-making: a qualitative analysis of the impact of a perioperative physician clinic

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033277
Author(s):  
Clarabelle T Pham ◽  
Catherine L Gibb ◽  
Robert A Fitridge ◽  
Jon Karnon ◽  
Elizabeth Hoon
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Elective Surgery ◽  
Structured Interviews ◽  
Complex Patients ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Additional Value ◽  
The Impact ◽  
High Risk Clinic

ObjectivePatients with comorbidities can be referred to a physician-led high-risk clinic for medical optimisation prior to elective surgery at the discretion of the surgical consultant, but the factors that influence this referral are not well understood. The aims of this study were to understand the factors that influence a surgeon’s decision to refer a patient to the clinic, and how the clinic impacts on the management of complex patients.DesignQualitative study using theoretical thematic analysis to analyse transcribed semi-structured interviews.SettingInterviews were held in either the surgical consultant’s private office or a quiet office/room in the hospital ward.ParticipantsSeven surgical consultants who were eligible to refer patients to the clinic.ResultsWhen discussing the factors that influence a referral to the clinic, all participants initially described the optimisation of comorbidities and would then discuss with examples the challenges with managing complex patients and communicating the risks involved with having surgery. When discussing the role of the clinic, two related subthemes were dominant and focused on the management of risk in complex patients. The participants valued the involvement of the clinic in the decision-making and communication of risks to the patient.ConclusionsThe integration of the high-risk clinic in this study appears to offer additional value in supporting the decision-making process for the surgical team and patient beyond the clinical outcomes. The factors that influence a surgeon’s decision to refer a patient to the clinic appear to be driven by the aim to manage the uncertainty and risk to the patient regarding surgery and it was seen as a strategy for managing difficult and complex cases.

scholarly journals PASIRENKAMO RIZIKOS LYGIO PASTOVUMAS ĮVAIRIOSE SPRENDIMŲ SITUACIJOSE

Psichologija ◽  
2003 ◽  
Vol 28 ◽  
pp. 7-18 ◽  
Author(s):  
Auksė Endriulaitienė ◽  
Vaclovas Martišius
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Risk Taking ◽  
Correlation Coefficients ◽  
Risk Level ◽  
Risky Decision ◽  
Risk Levels ◽  
Vilnius University ◽  
Social Scientific ◽  
The Impact

Kadangi gyvename didžiulio nerimo ir netikrumo laikais, pastaruoju metu socialinių mokslų atstovų domėjimasis rizika smarkiai padidėjo. Pagrindinė teorija, analizuojanti rizikingus sprendimus, yra D. Kahnemano ir A. Tversky?o sukurta prospektų teorija, kuri akcentuoja situacijos veiksnius, nulemiančius sprendimo rizikos lygį. Prieštaringi tyrimų rezultatai paskatino psichologus iškelti mintį, kad žmonės turi tam tikrą asmenybės savybę - polinkį rizikuoti. Šio tyrimo tikslas - nustatyti, ar yra įvairiose sprendimų situacijose pasirenkamo rizikos lygio, kuris gali būti asmenybės bruožo - polinkio rizikuoti rodiklis, ryšys. Tyrime dalyvavo 262 Vilniaus ir Vytauto Didžiojo universitetų studentai; jie pildė Pasirinkimo dilemų klausimyną bei Sprendimų priėmimo stiliaus skalę. Tyrimo rezultatai atskleidė, kad rizikos lygis, pasirenkamas įvairiose sprendimų situacijose, yra santykinai pastovus dydis ir gali būti susijęs su asmenybės bruožu - polinkiu rizikuoti. Taigi gauti tyrimo duomenys leidžia diskutuoti su tais psichologais, kurie ypač akcentuoja situacijos svarbą priimant rizikingus sprendimus ir nuvertina asmenybės bruožų įtaką žmogaus pasirinkimams. THE STABILITY OF RISK LEVEL ACROSS VARIOUS DECISION SITUATIONSAuksė Endriulaitienė, Vaclovas Martišius SummaryFew would dispute that we are living at a time of high anxiety and uncertainty. That's why in recent years, social scientific interest in risk has increased enormously. The main theory that explains human decision processes in the face of risk is Prospect theory of D. Kahneman and A. Tversky. It emphasizes the impact of situation upon human risk taking. The contradictory results from the investigations in the perspective of human trait theory give the challenge to the prospect theory and raise the assumption that people have the personality trait - propensity to risk that could explain at least the part of human risky decision making and risk taking behavior. The present investigation aims to find if the decision risk level is stable across various decision situations and could be the index of human propensity to risk. The subjects were 262 students from Vytautas Magnus University and Vilnius University in Lithuania; they completed Choice Dilemma Questionnaire (Kogan, Wallach, 1964) and The Scale of General Decision Making Style (Scott, Bruce, 1995). The results showed that correlation coefficients among risk levels in various decision situations were high and significant. So risk level was stable across the situations and could be related to the personality disposition - propensity to risk. The implications of results and limitations of the study were discussed.

scholarly journals Information load, time spent, and risk taking in complex decision making

1968 ◽  
Vol 13 (6) ◽  
pp. 327-328 ◽  
Author(s):  
Siegfried Streufert ◽  
Susan C. Streufert
Keyword(s):  
Decision Making ◽  
Risk Taking ◽  
Information Load ◽  
Complex Decision Making ◽  
Complex Decision

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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