Decision Theory Model Research Articles

The Formation of Job Referral Networks: Experimental Evidence from Urban Ethiopia

Unemployment is currently a major policy issue in many African countries (African Development Bank 2012). As pockets of widespread unemployment often persist despite labor market expansion, there is a growing demand among policy makers for research and policy tools that address poor labor market attachment. In this study we focus on exclusion from job contact networks, which constitutes a major disadvantage for labor market participants in settings where referral hiring is common and information about jobs hard to obtain. In a mid-size town in northern Ethiopia, where these mechanisms are at work, we observe that many individuals do not access local job contact networks. Models of strategic network formation and behavioral decision theory suggest that given the right incentives, job contact networks should be more inclusive. On these grounds we hypothesize that workers would link to peripheral peers when this maximizes their chances of referral and when self-regarding concerns are absent due to social preferences. We devise an experimental design adapted from Beaman and Magruder (2012) to test these hypotheses. Our results lend broad support to the assumption of strategic network formation. In a setting where competition for referrals makes connections with central players undesirable, agents tie preferably to peripheral peers. However, in treatments in which other-regarding considerations are made salient, players do not seem to choose links with peripheral peers more often than at random. Our findings suggest that a modification of field incentives, for example, via a reform in referral hiring procedures, can generate more inclusive job contact networks.

The Role of Folk Dance in the Processes of Individual and Social Wellbeing: A Comparison with Other Popular Recreational Activities Through Models of Decision Theory and Game Theory

A qualitative and quantitative study, in which the individual benefits and social benefits of folk dance are compared with those obtained from the most popular recreational activities and regenerative properties, physical and mental, individual and social, is presented. Our starting point consists in the definition of the basic objectives of each individual for achieving the physica l and mental wellbeing and a satisfactory placement in a pleasant and stimulating social environment. Moreover we compare some bodily activities, mental and otherwise designed to get these goals. Our present study is based on a qualitative analysis, quantitative models of decision theory, and a collection of opinions of experts and practitioners. In this framework we show that both subjective and objective benefits of folk dance practice are higher than those deriving from other activities taken in consideration, with respect to the same set of objectives. Furthermore, the folk dance has positive effects over the individual in the strict sense (improvement of physical and mental activities and personality) and in his relations with others. Our interpretative hypothesis of the achieved results is that the folk dance has some specific features that are present only in part in other activities, characteristics linked to the intimate nature of human beings that integrate and enhance each other.

Rationalizing Size, Value, and Momentum Effects with a CAPM2

This paper shows that a Capital Asset Pricing Model based on Continuous Asymmetric Polynomial Models (CAPM 2 ) can identify the sources of risk that drive the cross section of stock returns. In accordance with recent decision theory models, the CAPM 2 can price the key factors that drive risky choice behavior: (i) Goal Achievement (importance of the overall probability of obtaining positive payos), (ii) Loss Aversion (losses loom larger than gains), and (iii) preference for Security/Potential (downside risk aversion and preference for upside potential). These three factors are also the key drivers of size, value, and momentum portfolio returns. Therefore, size, value, and momentum factors do not load when they are tested on the CAPM 2 . Moreover, zero cost portfolios that take long (short) positions on securities with the highest (lowest) loadings on the three CAPM 2 factors deliver positive and statistically signicant

Reasoning under uncertainty and multi-criteria decision making in data privacy

By means of an integration of decision theory and probabilistic models, we explore and develop methods for improving data privacy. Our work encompasses disclosure control tools in statistical databases and privacy requirements prioritization; in particular we propose a Bayesian approach for the on-line auditing in Statistical Databases and Pairwise Comparison Matrices for privacy requirements prioritization. The first approach is illustrated by means of examples in the context of statistical analysis on the census and medical data, where no salary (resp. no medical information), that could be related to a specific employee (resp. patient), must be released; the second approach is illustrated by means of examples, such as an e-voting system and an e-banking service that have to satisfy privacy requirements in addition to functional and security ones. Several fields in the social sciences, economics and engineering will benefit from the advances in this research area: e-voting, e-government, e-commerce, e-banking, e-health, cloud computing and risk management are a few examples of applications for the findings of this research.

Geoengineering and Abatement: A 'Flat' Relationship Under Uncertainty

The potential of geoengineering as an alternative or complementary option to mitigation and adaptation has received increased interest in recent years. The scientific assessment of geoengineering is driven to a large extent by assumptions about its effectiveness, costs, and impacts, all of which are highly uncertain. This has led to a polarizing debate. This paper evaluates the role of Solar Radiation Management (SRM) on the optimal abatement path, focusing on the uncertainty about the effectiveness of SRM and the interaction with uncertain climate change response. Using standard economic models of dynamic decision theory under uncertainty, we show that abatement is decreasing in the probability of success of SRM, but that this relation is concave and thus that significant abatement reductions are optimal only if SRM is very likely to be effective. The results are confirmed even when considering positive correlation structures between the effectiveness of geoengineering and the magnitude of climate change. Using a stochastic version of an Integrated Assessment Model, the results are found to be robust for a wide range of parameters specification.

Moral Uncertainty in Bioethical Argumentation: A New Understanding of the 'Pro-Life' View on Early Human Embryos

In this paper I present a new interpretation of the ‘pro-life’ view on the status of early human embryos. In my understanding this position is based not on presumptions about the ontological status of embryos and their developmental capabilities but on the specific criteria of rational decisions under uncertainty and on a cautious response to the ambiguous status of embryos. This view, which uses the decision theory model of moral reasoning, promises to reconcile the uncertainty about the ontological status of embryos with the certainty about normative obligations. I will demonstrate that my interpretation of the ‘pro-life’ view, although seeming to be stronger than the standard one, has limited scope and cannot be used to limit destructive research on human embryos.

Wilde heuristics and Rum Tum Tuggers: preference indeterminacy and instability

Models in decision theory and game theory assume that preferences are determinate: for any pair of possible outcomes, a and b, an agent either prefers a to b, prefers b to a, or is indifferent as between a and b. Preferences are also assumed to be stable: provided the agent is fully informed, trivial situational influences will not shift the order of her preferences. Research by behavioral economists suggests, however, that economic and hedonic preferences are to some degree indeterminate and unstable, which in turn suggests that other sorts of preferences may suffer the same problem. Even fully informed agents do not always determinately prefer a to b, prefer b to a, or feel indifferent as between a and b. Seemingly trivial situational influences rearrange the order of their preferences. One could respond that decision theory and game theory are not meant to describe actual behavior, and that they instead adumbrate an ideal of rationality from which human action diverges in various ways. When the divergences are small and systematic, they help us identify the heuristics that conspire to help people approximate rationality. One such heuristic, dubbed the Wilde heuristic, is explored. However, the divergences documented by behavioral economists threaten to be too large to handle through idealization. The Rum Tum Tugger Model, in which indifference is intransitive, is spelled out as one promising way for decision and game theory to retrench. Preferences may be locally unstable and indeterminate, but when the differences between options are sufficiently large, they approximate stability and determinacy.

Stochastic improvement of inspection and maintenance of corroding reinforced concrete structures placed in unsaturated environments

Multiple constraints imposed by economic, social and environmental considerations lead maintenance planning optimization to a major challenge for designers, owners and users of infrastructure. This study focuses on the management of reinforced concrete (RC) structures placed in chloride-contaminated atmospheres. Under these exposure conditions, chloride penetration generates corrosion of the reinforcing bars reducing the RC durability. Therefore, modeling the deterioration process as well as the maintenance actions carried out during the operational life becomes paramount for the formulation of a comprehensive maintenance strategy. This paper presents a new methodology for improving the performance of maintenance strategies for corroding RC structures. The proposed approach is based on preventive maintenance and combines: a numerical model of chloride penetration, Markov processes and decision theory. The uncertainty related to material properties, model and environmental actions as well as the effect of imperfect inspections are also integrated in the model. The aforementioned points are illustrated with a numerical example. In general, the overall results indicate that the proposed methodology can be useful to improve the costs of a given maintenance strategy by ensuring appropriate levels of serviceability and safety.

Research Article: Determination of Solar Energy Transition Potential of Department of Defense Facilities and Nontactical Vehicles: An Application of Multicriteria Decision Theory Modeling and Simulation Approaches

The United States (US) Department of Defense (DoD) offers a great opportunity to implement solar energy solutions across its US bases. It is the single largest energy consumer in the federal govern...

A Multi-Agent Model for Overlapping Negotiations

In the last few years, research on multi-agent systems has addressed different aspects of intelligent negotiations using methods developed in different domains including game theory, decision theory, and economic models. The research proposed in Andersson and Sandholm (1999), Sandholm (1993), Sandholm and Lesser (1995) and Aknine et al. (2004), Aknine (2002) are significant examples. However, only some of this work focuses on problems related to complex negotiations, particularly those concerning new generation applications. This new research raises fundamental difficulties we have encountered, especially in overlapping negotiations and combined negotiations. This article is interested essentially in overlapping negotiations, which include several agent roles in a same negotiation. One or more agents may play each of these roles. This work shows that the high-level negotiation models are necessary in order to control the execution of overlapping negotiation processes, since, in these negotiations we are facing both classical problems of multi-agent negotiations based on two agents’ roles and the problems concerned with the interdependence of these negotiations. Synchronization of these different processes is thus necessary because of the multiplicity of the roles. Thus, this paper presents a formalized negotiation model, which deals with this problem. It gives a theoretical analysis of the suggested model and discusses the results of the experimental evaluation. To perform this evaluation, we use the application of intelligent service agencies on the Internet.

Predicting a recovery date from the economic crisis of 2008

Predicting a recovery from a crisis is always difficult, but it is particularly so with the 2008 crisis in the United States. How could a small segment of the financial markets known as subprime credit bring down the world’s largest economy into the worst recession since WWII? The resulting conflicts in policy responses are so severe that the short-term objective (recovery) clashes with the longer-term and more structural goals (governance, regulations, technology). This and the enormous uncertainties caused by it add to the difficulties to predict the pace of recovery. While the economic turnaround depends on consumers’ decision to spend and business’ decision to invest and hire, in an uncertain situation such decisions can only be taken as a result of market players’ perceptions of opportunity that depend on their emotional state and confidence. When the latter produces spontaneous urge to action (‘animal spirits’), the recovery process accelerates. Thus, the appropriate model to predict recovery should be able to incorporate such perceptions factors. By identifying and prioritizing economic and policy factors, it is shown how such a model, the Analytic Network Process (ANP), can be used to make the prediction of the recovery time of the US economy. The forecast was made during Spring 2009 by the author working with participants in a seminar of “Economics of Financial Crisis” at Cornell University. We used an expert judgment approach within the framework of a decision theory model, based on the ANP structure that captures the interplay between financial market, housing sector, and market confidence, all of which are influenced by a range of policies. It is estimated that a real sustainable recovery will begin around late July or early August 2010. While a quicker recovery is possible given the enormous size of fiscal stimulus, monetary injection and unprecedented measures of qualitative easing, it is our conjecture that the temporary nature of all these measures will make such a quick turn-around unsustainable (a double-dip recession). When sensitivity analysis was performed, it was found that altering the priorities of the policies, and their interactions with the aggregate demand components, would not significantly change the estimated time to recovery. This stability of the prediction is due to the overriding importance of restoring confidence, making the other factors less important.

Doping und Wettbewerbsintensität

Abstract There are systematic incentives to dope within the structure of sports tournaments. A simple decision theory model demonstrates the nature of these incentives and the circumstances in which they are particularly strong. From this model, some empirically testable hypotheses are derived. The most important one, that greater competition between athletes induces more doping, is corroborated by comparing the competitiveness and number of caught dopers in thirteen different athletic sports. The lack of other significant findings may be explained by the decisive role of competitiveness which is more powerful than all other effects and is also a central element of them. Zusammenfassung In Sportwettkampfen existieren systematische Anreize zu dopen. Ein einfaches entscheidungstheoretisches Modell erklart diese Anreize und zeigt, unter welchen Umstanden sie besonders stark sind. Aus diesem Modell werden empirisch testbare Hypothesen abgeleitet. Die wichtigste Hypothese, dass starkerer Wettbewerb zwischen Sp...

Die Inanspruchnahme formeller Hilfen durch Menschen mit problematischem oder pathologischem Glücksspielverhalten

Up to now, people with problematic or pathological gambling behaviour are only being marginally reached by the professional helping network. By searching Medline and WISO databases, original studies addressing the factors that influence the utilization process were identified and evaluated using a decision theory model. Results showed that people with problematic or pathological gambling behaviour have specific barriers to overcome compared to people suffering from other addictions. The most important determinants which prevent a person from seeking help can generally be found at the level of the individual. Severity of gambling addiction and associated negative financial and psychosocial consequences were found to have a positive influence on the utilization of formal help. Structural aspects of the helping agencies were found to be less important. Conclusions concerning possible improvements of accessibility are drawn.

Statistical decision theory to relate neurons to behavior in the study of covert visual attention

Scrutiny of the numerous physiology and imaging studies of visual attention reveal that integration of results from neuroscience with the classic theories of visual attention based on behavioral work is not simple. The different subfields have pursued different questions, used distinct experimental paradigms and developed diverse models. The purpose of this review is to use statistical decision theory and computational modeling to relate classic theories of attention in psychological research to neural observables such as mean firing rate or functional imaging BOLD response, tuning functions, Fano factor, neuronal index of detectability and area under the receiver operating characteristic (ROC). We focus on cueing experiments and attempt to distinguish two major leading theories in the study of attention: limited resources model/increased sensitivity vs. selection/differential weighting. We use Bayesian ideal observer (BIO) modeling, in which predictive cues or prior knowledge change the differential weighting (prior) of sensory information to generate predictions of behavioral and neural observables based on Gaussian response variables and Poisson process neural based models. The ideal observer model can be modified to represent a number of classic psychological theories of visual attention by including hypothesized human attentional limited resources in the same way sequential ideal observer analysis has been used to include physiological processing components of human spatial vision (Geisler, W. S. (1989). Sequential ideal-observer analysis of visual discrimination. Psychological Review 96, 267–314.). In particular we compare new biologically plausible implementations of the BIO and variant models with limited resources. We find a close relationship between the behavioral effects of cues predicted by the models developed in the field of human psychophysics and their neuron-based analogs. Critically, we show that cue effects on experimental observables such as mean neural activity, variance, Fano factor and neuronal index of detectability can be consistent with the two major theoretical models of attention depending on whether the neuron is assumed to be computing likelihoods, log-likelihoods or a simple model operating directly on the Poisson variable. Change in neuronal tuning functions can also be consistent with both theories depending on whether the change in tuning is along the dimension being experimentally cued or a different dimension. We show that a neuron’s sensitivity appropriately measured using the area under the Receive Operating Characteristic curve can be used to distinguish across both theories and is robust to the many transformations of the decision variable. We provide a summary table with the hope that it might provide some guidance in interpreting past results as well as planning future studies.

Resource selection in grid: a taxonomy and a new system based on decision theory, case‐based reasoning, and fine‐grain policies

AbstractResource selection on current brokers still presents challenges for achieving the best solution in the decision‐making process, especially when considering many factors. We approach this problem considering user preference for specific resource selection objectives such as expected performance for an application execution, resource access restriction, execution application cost, and resource reliability. For each objective, we employ different techniques and combine them in a decision theory model. By considering performance in the selection process, we use the case‐based reasoning technique based on similar past job executions to predict a new job time execution. With the resource access restriction as a selection factor, we develop a fine‐grain policy‐based model for distributed resource access verification. Unlike a global access policy, which applies to all resources in a virtual organization, a fine‐grain policy establishes rules for specific resources and users. In this case, a previous access restriction verification prevents a resource selection, which may deny access to a requisition, resulting in an unsuccessful submission. The decision model uses the multi‐attribute utility theory, which relates the important objectives above and allows different proportions of user preferences for each objective. The complete solution is distributed and implemented using a multi‐agent system, acting as a resource broker. All models of this paper are analyzed in a real environment, presenting appropriate functional behaviors. Results show that our prediction model is accurate and efficient in the prediction process and our distributed model runs faster than centralized approaches and considers access restriction heterogeneities. Copyright © 2008 John Wiley & Sons, Ltd.

Hydrometeorological Short-Range Ensemble Forecasts in Complex Terrain. Part II: Economic Evaluation

Abstract Two economic models are employed to perform a value assessment of short-range ensemble forecasts of 24-h precipitation probabilities for hydroelectric reservoir operation. Using a static cost–loss model, the value of the probability information is compared to the values of a deterministic control high-resolution forecast and of an ensemble-average forecast for forecast days 1 and 2. It is found that the probabilistic ensemble forecast provides value to a much wider range of hydroelectric operators than either the deterministic high-resolution forecast or the ensemble-average forecast, although for a small subset of operators the value of the three forecasts is the same. Forecasts for day-1 precipitation provide measurably higher value than forecasts for day-2 precipitation because of the loss of skill in the longer-range forecasts. A decision theory model provides a continuous-variable weighting of a user-specific utility function. The utility function weights are supplied by the ensemble prediction system, and the outcome is compared with weights calculated from a deterministic model, from the ensemble average, and from climatology. It is found that the methods employing the full ensemble and the ensemble average outperform the single deterministic model and climatology for the hydroelectric reservoir scenario studied.

Asymmetries in Information Processing in a Decision Theory Framework

Research in psychology suggests that some individuals are more sensitive to positive than to negative information while others are more sensitive to negative rather than positive information. I take these cognitive positive–negative asymmetries in information processing to a Bayesian decision-theory model and explore its consequences in terms of decisions and payoffs. I show that in monotone decision problems economic agents with more positive-responsive information structures are always better off, ex ante, when they face problems where payoffs are relatively more sensitive to the action chosen when the state of nature is favorable.

Indicators of Usefulness of Test Scores

Current thinking on validity suggests that educational institutions and individuals should evaluate their uses of test scores in the context of their fundamental goals. Regression coefficients and other traditional criterion-related validity statistics provide relevant information, but often do not, by themselves, address the fundamental reasons for using test scores. Formal decision theory models provide a logically rigorous way to do this, but they are difficult to implement in practice. This article considers a simplification of formal decision theory models, in which one estimates the proportion of examinees for whom positive outcomes result from a use of test scores. For uses involving selection, the proportion of examinees with positive outcomes can be calculated by applying traditional regression coefficients to the marginal distribution of scores in the unselected population. The incremental usefulness of using a particular variable can be judged by comparing its proportion to that associated with no selection and to that associated with using another variable, either alone or jointly. Examples, related to college admission and retention, are given to illustrate these ideas.

Qualitative decision impact model: A tri‐partite broadcast social network approach

AbstractDecision theory is the mathematics of making decisions under certain conditions of uncertainty. A major shortcoming in the application of decision theory to human problem‐solving is that it is difficult to sufficiently account for the influence of specific instances of human social‐psychological conditions in making value judgments. Equally challenging to decision theory are the complexity and diversity of human problems. The P2h.D. decision impact model describes the configuration of a structured and intentionally sustained network of podcasts, blogs, and wikis to provide informal resources and examples of human experience to supplement formal resources developed out of decision theory modeling.

Determining Cutoff Scores on a Developmental Screening Measure: Use of Receiver Operating Characteristics and Item Response Theory

Two different theoretical approaches were compared to determine the optimal cutoff scores for the Ages and Stages Questionnaires: Social-Emotional (ASQ: SE), a social-emotional screening test. Cutoff scores based on statistical decision theory modeling, Receiver Operator Characteristics (ROC), were compared with cutoff scores obtained using Item Response Theory (IRT) measurement theory modeling. The purposes of this comparison were (a) to offer alternative perspectives on the computation and function of cutoff scores, and (b) to obtain appropriate cutoff scores for the ASQ: SE screening test. Results are presented for both analyses, ROC and IRT Collectively, results from the two approaches offer comparable solutions to persistent problems arising when screening decision rules are needed. Results underscore the psychometric integrity of the ASQ: SE cutoff scores.