The role of risk aversion in non-conscious decision making

To what extent can people choose advantageously without knowing why they are making those choices? This hotly debated question has capitalized on the Iowa Gambling Task (IGT), in which people often learn to choose advantageously without appearing to know why. However, because the IGT is unconstrained in many respects, this finding remains debated and other interpretations are possible (e.g., risk aversion, ambiguity aversion, limits of working memory, or insensitivity to reward/punishment can explain the finding of the IGT). Here we devised an improved variant of the IGT in which the deck-payoff contingency switches after subjects repeatedly choose from a good deck, offering the statistical power of repeated within-subject measures based on learning the reward contingencies associated with each deck. We found that participants exhibited low confidence in their choices, as probed with post-decision wagering, despite high accuracy in selecting advantageous decks in the task, which is putative evidence for non-conscious decision making. However, such a behavioral dissociation could also be explained by risk aversion, a tendency to avoid risky decisions under uncertainty. By explicitly measuring risk aversion for each individual, we predicted subjects’ post-decision wagering using Bayesian modeling. We found that risk aversion indeed does play a role, but that it did not explain the entire effect. Moreover, independently measured risk aversion was uncorrelated with risk aversion exhibited during our version of the IGT, raising the possibility that the latter risk aversion may be non-conscious. Our findings support the idea that people can make optimal choices without being fully aware of the basis of their decision. We suggest that non-conscious decision making may be mediated by emotional feelings of risk that are based on mechanisms distinct from those that support cognitive assessment of risk

Locating fire-stations: an integrated approach for Belgium

This paper demonstrates the potential of a decision-support system developed for Belgium by a consortium of universities and a private firm, in the framework of a public call by the Ministry of the Interior. The system is designed to provide the Belgian emergency management administration with a complete decision-aid tool for the location of fire-stations. The originality of the project is that it includes a risk-modeling approach developed at a national scale. This analysis involves a multiscale GIS system which includes a thorough representation of the physical, human and economic spatial realities, a risk modeling approach, an adequate optimal location and allocation model (taking into account both queuing and staffing problems). The final result is an interactive operational tool for defining locations, equipment allocations, staffing, response times, the cost/efficiency trade-off, etc. which can be used in an assessment as well as a prospective context. It has numerous functionalities including rapid modification of the modeling conditions to allow for quick scenario analysis, multiscale analysis, and prospective analysis.ocation-allocations, GIS, fire-stations, Belgium

"Last-Mile" preparation for a potential disaster

Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of small-scale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socio-economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity

Systemic Approach to Estimation of Financial Risks

Modern approaches to risk estimation, forecasting and management are based upon intensive application of mathematical modeling, estimation theory, application of Bayesian statistics, simulation, decision making methods and techniques and other approaches [1]. One of the most suitable instrumentations for risk analysis and management create informational decision support systems (DSS) that are widely used for solving different problems from the realms of forecasting, control, medical and engineering diagnostics, planning and management

A Novel People-Centered Approach to Modeling and Decision Making on Future Earthquake Risk

Numerous approaches to earthquake risk modeling and quantification have already been proposed in the literature and/or are well established in practice. However, most of these procedures are designed to focus on risk in the context of current static exposure and vulnerability, and are therefore limited in their ability to support decisions related to the future, as yet partially unbuilt, urban landscape. This paper outlines an end-to-end risk modeling framework that explicitly addresses this specific challenge. The framework is designed to consider the earthquake risks of tomorrow's urban environment, using a simulationbased approach to rigorously capture the uncertainties inherent in future projections of exposure as well as physical and social vulnerability. The framework also advances the state-of-practice in future disaster risk modeling by additionally: (1) providing a harmonized methodology for integrating physical and social impacts of disasters that facilitates flexible characterization of risk metrics beyond physical damage/asset losses; and (2) incorporating a participatory, people-centered approach to riskinformed decision making. It can be used to support decision making on policies related to future urban planning and design, accounting for various stakeholder perspectives on risk

A Simulation Model for Decision Support in Business Continuity Planning

Enterprises with a global supply network are at risk of lost revenue as a result of disruptive disasters at supplier locations. Various strategies exist for addressing this risk, and a variety of types of research has been done regarding the identification, assessment and response to the risk of disruption in a supply chain network. This thesis establishes a decision model to support Business Continuity Planning at the first-tier supplier level. The decision model incorporates discrete-event simulation of supply chain networks (through Simio software), Monte Carlo simulation, and risk index optimization. After modeling disruption vulnerability in a supply chain network, costs of implementing all combinations of Business Continuity Plans are ranked and then tested in discrete-event simulation for further insight into inventory levels, unmet customer demand, production loss and related costs. A case study demonstrates the implementation of the decision support process and tests a historical set of data from a large manufacturing company. Discrete-event simulation modeling of loss is confirmed to be accurate. The relevance of the model concept is upheld and recommendations for future work are made

Systemic Approach to Estimation of Financial Risks

Modern approaches to risk estimation, forecasting and management are based upon intensive application of mathematical modeling, estimation theory, application of Bayesian statistics, simulation, decision making methods and techniques and other approaches [1]. One of the most suitable instrumentations for risk analysis and management create informational decision support systems (DSS) that are widely used for solving different problems from the realms of forecasting, control, medical and engineering diagnostics, planning and management

Stochastic Risk Analysis Of Budgeted Financial Statements

Stochastic modeling of financial statements facilitates risk analysis by explicitly introducing uncertainty for key input variables.  When input variables are modeled as probability distributions, then Monte Carlo simulation can be performed for the budgeted financial statements.  Critical outputs within the financial statements can be displayed with cumulative graphs that show a range of outcomes with its likelihood of occurrence.  Stochastic modeling techniques are superior to scenario analysis in assessing risk and are another innovative use of technology in support of managerial decision-making.  Students for a cost/managerial accounting course reported a better understanding of risk analysis for accounting relationships, and a greater interest in modeling uncertainty in other financial relationships

Using Curvilinear Spline Regression To Empirically Test Relationships Predicted By Prospect Theory

Prospect theory (Kahneman & Tversky, 1979) suggests that decision makers compare decision criteria against a reference point when evaluating alternatives. Specifically it posits that decision makers are risk-seeking for losses (below the reference point) and risk-averse for gains (above the reference point).  It further proposes that the degree of risk aversion above the reference point is greater than the risk seeking below it.  This theory has received widespread acceptance due to intuitive appeal and theoretical support.  However the theory does not have strong evidentiary support in actual practice because it is rarely empirically tested in non-experimental situations involving real market data.  Often the type or amount of data available does not lend itself to the examination of relationships posited by prospect theory, however even if he data is appropriate, difficulties may arise in modeling and testing.  In this paper, after a brief discussion of prospect theory and situations where it is applicable, we present an approach to the empirical testing of prospect theory predictions using curvilinear spline (piecewise polynomial) regression.  Among the issues addressed are adequacy of data, choice of inflection point, modeling the curves and hypothesis testing

Decision Support Tools for Cloud Migration in the Enterprise

This paper describes two tools that aim to support decision making during the migration of IT systems to the cloud. The first is a modeling tool that produces cost estimates of using public IaaS clouds. The tool enables IT architects to model their applications, data and infrastructure requirements in addition to their computational resource usage patterns. The tool can be used to compare the cost of different cloud providers, deployment options and usage scenarios. The second tool is a spreadsheet that outlines the benefits and risks of using IaaS clouds from an enterprise perspective; this tool provides a starting point for risk assessment. Two case studies were used to evaluate the tools. The tools were useful as they informed decision makers about the costs, benefits and risks of using the cloud.Comment: To appear in IEEE CLOUD 201