EURO Journal on Decision Processes最新文献

This paper develops a decision evaluation framework to assess how the treatment of risk affects the reliability of, and investment into, electricity generation infrastructure. First, portfolios of electricity generation technologies that comprise the energy supply systems in the US are evaluated using a mean-variance approach. Second, this research assesses the reliability of the portfolios with the aid of loss of load expectation and loss of energy expectation metrics. The methodology considers the least-cost technology mix coupled with the reduction of market and system risks. The variation in the portfolio cost is based on the prevailing policies in the geographic locations. Overall, the current mix of technologies evaluated along the cost-risk latitudes shows an inefficient electricity technology portfolio system. First, investments in renewable technologies may create a bifurcation. On the one hand, the portfolios with significant proportions of the high-cost intermittent technologies exhibit low market risks. On the other hand, these portfolios have less desirable system reliability measures. Second, policy makers will find it instructive that a more diverse electricity technology mix offers the potential to migrate to the efficient frontier in the near term. However, it is imperative to craft policies in support of the transition with the caveat that technology diversity is not always a panacea for improving system reliability even if the portfolio is on the efficient frontier. This work projects some intriguing insights and offers guidance for policy makers.

Determining a fair price and an appropriate timescale to trade municipal debt is a complex decision. This research uses data informatics to explore transaction characteristics and trading activity of investment grade US municipal bonds. Using the relatively recent data stream distributed by the Municipal Securities Rulemaking Board, we provide an institutional summary of market participants and their trading behavior. Subsequently, we focus on a sample of AAA bonds to derive a new methodology to estimate a trade-weighted benchmark municipal yield curve. The methodology integrates the study of ridge regression, artificial neural networks, and support vector regression. We find an enhanced radial basis function artificial neural network outperforms alternate methods used to estimate municipal term structure. This result forms the foundation for establishing a decision theory on optimal municipal bond trading. Using multivariate modeling of a liquidity domain measured across three dependent variables, we investigate the proposed decision theory by estimating weekly production-theoretic bond liquidity returns to scale. Across the three liquidity measures and for almost all weeks investigated, bond trading liquidity is elastic with respect to the modeled factors. This finding leads us to conclude that an optimal trading policy for municipal debt can be implemented on a weekly timescale using the elasticity estimates of bond price, trade size, risk, days-to-maturity, and the macroeconomic influences of labor in the workforce and building activity.

The volume and availability of business and finance data may continue to increase in the near future. However, the utility of such data is by no means straightforward due to a lack of integration between data-driven techniques and usual decision-making processes. This paper aims to integrate data with multiobjective decision-making in cash management by means of machine learning. To this end, we first consider cash flow forecasting as a data-driven procedure to be used as a key input to multiobjective cash management problem in which both cost and risk are goals to minimize. Next, we compute the forecasting premium, namely, how much value can be achieved in exchange of predictive accuracy. Finally, we provide cash managers with a general methodology to improve decision-making in cash management through the use of data and machine learning techniques. This methodology is based on a novel closed-loop procedure in which the estimated forecasting premium (if any) is used as a critical feedback information to find better forecasting models and, ultimately, better cost-risk results in cash management.

Conditional preference networks (CP-nets) provide a compact and intuitive graphical tool to represent the preferences of a user. However, learning such a structure is known to be a difficult problem due to its combinatorial nature. We propose, in this paper, a new, efficient, and robust query-based learning algorithm for acyclic CP-nets. In particular, our algorithm takes into account the contradictions between multiple users’ preferences by searching in a principled way the variables that affect the preferences. We provide complexity results of the algorithm, and demonstrate its efficiency through an empirical evaluation on synthetic and on real databases.

Since the early applications of operations research and management science techniques in corporate financial management, financial decision support has evolved to a multi-disciplinary field combing different analytical approaches and technologies for supporting the decision-making process for financial problems faced by firms, organizations, and individuals. This paper provides an overview of the nature of financial decision support and its contributions, covering past developments and advances, as well as current trends and emerging topics on methodological, application, and implementation issues.

Biases like overconfidence and anchoring affect values elicited from people in predictable ways—due to people’s inherent cognitive processes. The more-or-less elicitation (MOLE) process takes insights from how biases affect people’s decisions to design an elicitation process to mitigate or eliminate bias. MOLE relies on four, key insights: (1) uncertainty regarding the location of estimates means people can be unwilling to exclude values they would not specifically include; (2) repeated estimates can be averaged to produce a better, final estimate; (3) people are better at relative than absolute judgements; and, (4) consideration of multiple values prevents anchoring on a particular number. MOLE achieves these by having people repeatedly choose between options presented to them by the computerized tool rather than making estimates directly, and constructing a range logically consistent with (i.e., not ruled out by) the person’s choices in the background. Herein, MOLE is compared, across four experiments, with eight elicitation processes—all requiring direct estimation of values—and is shown to greatly reduce overconfidence in estimated ranges and to generate best guesses that are more accurate than directly estimated equivalents. This is demonstrated across three domains—in perceptual and epistemic uncertainty and in a forecasting task.

This paper develops an R&D decision-making model in the real options framework. The model is generic enough to capture three types of uncertainty in an R&D project, namely, uncertainty of research duration and costs, market value of technology, and a competitor’s technology development. I derive analytical solutions, which help practitioners and researchers to evaluate various cases of R&D investment. Further, by analyzing the model with a wide range of parameter values, I reveal the following effects of the three types of uncertainty on R&D investment: higher uncertainty of research duration and costs, unlike market value uncertainty, speeds up investment, especially combined with a higher risk of competition. The investment timing can be U-shaped in the strength of competition because of the trade-off between the preemptive investment effect and the decreased project value effect. These results can account for empirical findings about the uncertainty–investment relation in industries with high R&D intensity and severe competition.

The occurrence of unexpected events that are extreme in magnitude, rare in frequency, and short-lived in duration poses distinctive challenges to decision makers and planners. In this paper we examine the impact of negative versions of these events, which we term “shocks”, on the judgmental forecasts of subjects experiencing them. A behavioral experiment asking participants to forecast monthly time series in the presence of temporary but extreme decreases in those series is used. Average changes to annual prediction intervals and 1-month ahead forecasts were much smaller than the magnitude of the shock and occurred in proportion to the size of the shock. Changes to prediction intervals were more persistent for moderate than large shocks, and larger for shocks occurring a second time. Our results provide supporting evidence for the view that decision makers underweight rare and extreme events rather than overweight them, consistent with a discounting or forgetting effect. The behavioral findings are relevant to operations researchers involved in expert judgment elicitation and in supporting decision making.