We firstly redefine the operations of Molodtsov’s soft sets to make them more functional for improving several new results. We also define products of soft sets and
uni–
int decision function. By ...using these new definitions we then construct an
uni–
int decision making method which selects a set of optimum elements from the alternatives. We finally present an example which shows that the method can be successfully applied to many problems that contain uncertainties.
Research Summary: We develop a behavioral theory of real options that relaxes the informational and behavioral assumptions underlying applications of financial options theory to real assets. To do ...so, we augment real option theory's focus on uncertain future asset values (prospective uncertainty) with feedback learning theory that considers uncertain current asset values (contemporaneous uncertainty). This enables us to incorporate behavioral bias in the feedback learning process underlying the option execution/termination decision. The resulting computational model suggests that firms that inappropriately account for contemporaneous uncertainty and are subject to learning biases may experience substantial downside risk in undertaking real options. Moreover, contrary to the standard option result, greater uncertainty may decrease option value, making commitment to an investment path more effective than remaining flexible. Managerial Summary: Executives recognize the need to make uncertain investments to grow their business while mitigating downside risk. The analogy between financial options and real corporate investments provides an appealing method to consider the practical challenge of such investment decisions. Unfortunately, the "real options" analogy seems to break down in practice. We identify how a second form of uncertainty confounds real options intuition, leading managers to overestimate the value of uncertain investments. We present a behavioral real options model that accounts for both forms of uncertainty and suggest how uncertainty interacts with behavioral bias in the option execution/termination decision. Our model facilitates assessment of the conditions under which investments in uncertain opportunities are usefully considered as real options, and provides a means to evaluate their attractiveness.
Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical ...issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making.
Due to the uncertainty of decision environment and differences of decision makers’ culture and knowledge background, multi-granular HFLTSs are usually elicited by decision makers in a multi-attribute ...group decision making (MAGDM) problem. In this paper, a novel consensus model is developed for MAGDM based on multi-granular HFLTSs. First, it is defined the group consensus measure based on the fuzzy envelope of multi-granular HFLTSs. Afterwards, an optimization model which aims to minimize the overall adjustment amount of decision makers’ preference is established. Based on the model, an iterative algorithm is devised to help decision makers reach consensus in MAGDM with multi-granular HFLTSs. Numerical results demonstrate the characteristics of the proposed consensus model.
Every day, coalition cabinets make policy decisions critical to international politics. Juliet Kaarbo examines the dynamics of these multiparty cabinets in parliamentary democracies in order to ...assess both the quality of coalition decision making and the degree to which coalitions tend to favor peaceful or military solutions. Are coalition cabinets so riddled by conflict that they cannot make foreign policy effectively, or do the multiple voices represented in the cabinet create more legitimate and imaginative responses to the international system? Do political and institutional constraints inherent to coalition cabinets lead to nonaggressive policies? Or do institutional and political forces precipitate more belligerent behavior?
Employing theory from security studies and political psychology as well as a combination of quantitative cross-national analyses and twelve qualitative comparative case studies of foreign policy made by coalition cabinets in Japan, the Netherlands, and Turkey, Kaarbo identifies the factors that generate highly aggressive policies, inconsistency, and other policy outcomes. Her findings have implications not merely for foreign policy but for all types of decision making and policy-making by coalition governments.
Research Summary
We evaluate a pilot in an Inspections Department to explore the returns to a pair of algorithms that varied in their sophistication. We find that both algorithms provided substantial ...prediction gains, suggesting that even simple data may be helpful. However, these gains did not result in improved decisions. Inspectors often used their decision authority to override algorithmic recommendations, partly to consider other organizational objectives without improving outcomes. Interviews with 55 departments find that while some ran pilots seeking to prioritize inspections using data, all provided considerable decision authority to inspectors. These findings suggest that for algorithms to improve managerial decisions, organizations must consider both the returns to algorithms in the context and how decision authority is managed.
Managerial Summary
We evaluate a pilot in an Inspections Department to explore the returns to algorithms on decisions. We find that the greatest gains in this context come from integrating data into the decision process in the form of simple heuristics, rather than from increasing algorithmic sophistication or additional data. We also find that these improvements in prediction do not fully translate into improved decisions. Decision‐makers were less likely to follow data‐driven recommendations, partly in consideration of other organizational objectives, but without substantially improving on them overall. These findings suggest that organizations should consider the returns to technical sophistication in each context, and that the design and management of decision authority can be a key choice that impacts the value organizations can capture from using predictive analytics.
Objective
Clinical options for managing nonmetastatic prostate cancer (PCa) vary. Each option has side effects associated with it, leading to difficulty in decision‐making. This study aimed to assess ...the relationship between patient involvement in treatment decision‐making and subsequent decision regret (DR), and quantify the impact of health‐related quality of life (HRQL) outcomes on DR.
Methods
Men living in the United Kingdom, 18 to 42 months after diagnosis of PCa, were identified from cancer registration data and sent a questionnaire. Measures included the Decision Regret Scale (DRS), Expanded Prostate cancer Index Composite short form (EPIC‐26), EQ‐5D‐5L, and an item on involvement in treatment decision‐making. Multivariable ordinal regression was utilized, with DR categorized as none, mild, or moderate/severe regret.
Results
A total of 17 193 men with stage I‐III PCa completed the DRS: 36.6% reported no regret, 43.3% mild regret, and 20.0% moderate/severe regret. The odds of reporting DR were greater if men indicated their views were not taken into account odds ratio (OR = 6.42, 95% CI: 5.39‐7.64) or were involved “to some extent” in decision‐making (OR = 4.63, 95% CI: 4.27‐5.02), compared with men who were “definitely” involved. After adjustment, including for involvement, men reporting moderate/big problems with urinary, bowel, or sexual function were more likely to experience regret compared with men with no/small problems. Better HRQL scores were associated with lower levels of DR.
Conclusions
This large‐scale study demonstrates the benefit of patient involvement in treatment decision‐making for nonmetastatic PCa. However, men experiencing side effects and poorer HRQL report greater DR. Promoting engagement in clinical decision‐making represents good practice and may reduce the risk of subsequent regret.
What explains variability in norms of cooperation across organizations and cultures? One answer comes from the tendency of individuals to internalize typically successful behaviors as norms. ...Different institutional structures can cause different behavioral norms to be internalized. These norms are then carried over into atypical situations beyond the reach of the institution. Here, we experimentally demonstrate such spillovers. First, we immerse subjects in environments that do or do not support cooperation using repeated prisoner’s dilemmas. Afterwards, we measure their intrinsic prosociality in one-shot games. Subjects from environments that support cooperation are more prosocial, more likely to punish selfishness, and more trusting in general. Furthermore, these effects are most pronounced among subjects who use heuristics, suggesting that intuitive processes play a key role in the spillovers we observe. Our findings help to explain variation in one-shot anonymous cooperation, linking this intrinsically motivated prosociality to the externally imposed institutional rules experienced in other settings.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2015.2168
.
This paper was accepted by Uri Gneezy, behavioral economics.
Shared decision making (SDM) in mental health care involves clinicians and patients working together to make decisions. The key elements of SDM have been identified, decision support tools have been ...developed, and SDM has been recommended in mental health at policy level. Yet implementation remains limited. Two justifications are typically advanced in support of SDM. The clinical justification is that SDM leads to improved outcome, yet the available empirical evidence base is inconclusive. The ethical justification is that SDM is a right, but clinicians need to balance the biomedical ethical principles of autonomy and justice with beneficence and non‐maleficence. It is argued that SDM is “polyvalent”, a sociological concept which describes an idea commanding superficial but not deep agreement between disparate stakeholders. Implementing SDM in routine mental health services is as much a cultural as a technical problem. Three challenges are identified: creating widespread access to high‐quality decision support tools; integrating SDM with other recovery‐supporting interventions; and responding to cultural changes as patients develop the normal expectations of citizenship. Two approaches which may inform responses in the mental health system to these cultural changes – social marketing and the hospitality industry – are identified.
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