Uncertain Future Events Research Articles

Statement on Prediction Markets

Prediction markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. Using these markets as forecasting tools could substantially improve decision making in the private and public sectors. We argue that U.S. regulators should lower barriers to the creation and design of prediction markets by creating a safe harbor for certain types of small stakes markets. We believe our proposed change has the potential to stimulate innovation in the design and use of prediction markets throughout the economy, and in the process to provide information that will benefit the private sector and government alike.

How Do Primates Anticipate Uncertain Future Events?

The timing of an upcoming event depends on two factors: its temporal position, proximal or distal with respect to the present moment, and the unavoidable stochastic variability around this temporal position. We searched for a general mechanism that could describe how these two factors influence the anticipation of an upcoming event in an oculomotor task. Monkeys were trained to pursue a moving target with their eyes. During a delay period inserted before target motion onset, anticipatory pursuit responses were frequently observed. We found that anticipatory movements were altered by the temporal position of the target. Increasing the timing uncertainty associated with the stimulus resulted in an increase in the width of the latency distribution of anticipatory pursuit. These results show that monkeys relied on an estimation of the changing probability of target motion onset as time elapsed during the delay to decide when to initiate an anticipatory smooth eye movement.

Multi-Criteria Decision Making and composting of waste in the municipality of Bacoor in the Philippines

Choosing the most appropriate and practical strategy for solid waste management is a complex undertaking. It requires the identification of relevant perspectives, meaningful criteria, and sound evaluation method. The municipality of Bacoor in the Philippines used Multi-Criteria Decision Making (MCDM) method to arrive at a chosen waste management strategy - composting - from a holistic set of perspectives. However, the successful implementation of the chosen strategy was proven to depend not only on a sound decision process but also on the reliability and accuracy of projecting uncertain future events.

Dissociative experiences and mental imagery in undergraduate students: When mental images are used to foresee uncertain future events

In the present study we investigated the relationship between dissociation and mental imagery, by examining whether the frequency of dissociative experiences was related to the intentional generation of visual mental images of uncertain future events. A significant association was found, indicating that the higher the frequency of dissociative experiences, the stronger the tendency to generate mental images of future events. Regression analysis indicated that this association was moderated by the perceived emotional value of the event, but not by the global preference for image code. Costs and benefits of this use of mental imagery on cognitive functioning of high dissociators are discussed.

An in-depth analysis of information markets with aggregate uncertainty

The novel idea of setting up Internet-based virtual markets, information markets, to aggregate dispersed information and predict outcomes of uncertain future events has empirically found its way into many domains. But the theoretical examination of information markets has lagged relative to their implementation and use. This paper proposes a simple theoretical model of information markets to understand their information dynamics. We investigate and provide initial answers to a series of research questions that are important to understanding how information markets work, which are: (1) Does an information market converge to a consensus equilibrium? (2) If yes, how fast is the convergence process? (3) What is the best possible equilibrium of an information market? and (4) Is an information market guaranteed to converge to the best possible equilibrium?

A New Approach for Regulating Information Markets

Information markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. They have the potential to improve decision making and policies throughout the economy. The demand for information markets appears to be increasing. At the same time, there are regulatory hurdles to establishing such markets, largely arising from state prohibitions on Internet gambling. This paper reviews the current regulatory structure for information markets in the United States and offers recommendations for reform. We make two points: first, the authority for regulating many information markets should be shifted from the states to the federal government. Second, the federal government should implement a clear policy that would allow a large number of information market contracts. We argue that the Commodities Futures Trading Commission should regulate certain kinds of information market contracts that are futures contracts. Particular contracts should satisfy an economic purpose test administered by the CFTC. That test should consider whether an information market contract would allow for significant financial hedging or improve economic decisions. In addition, some types of information markets, such as over-the-counter markets, should remain exempt from CFTC regulation altogether. We believe that the effect of our proposal would be to enhance the development of information markets that improve economic decision making.

Harnessing the Power of Information: A New Approach to Economic Development

This paper offers a new approach to economic development, which we call performance-based policy. The basic idea is to get better information to implement better development decisions. The approach combines the use of information markets with payments for performance. An information market is a market for a contract that yields a payment based on the outcome of an uncertain future event, such as the number of people infected by HIV in Africa in 2010. We show how these markets can provide real-time information on the likely benefits and costs of different development projects. We argue that information markets combined with pay-for-performance contracts have the potential to revolutionize the way aid agencies, foundations, non-governmental organizations, and the private sector promote economic development. In addition to providing economic benefits, performance-based policy could lead to greater accountability and transparency in economic development. Despite its great potential, the approach has some limitations, particularly in information markets with little trading activity.

Prediction Markets

We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market's expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market's beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.

Effective Information for Offline Stochastic Feedback and Optimal Control of Dynamic Systems

The impact of uncertain future events on decision making in a stochastic environment is modeled in this paper. Such modeling is presented for both feedback and optimal control problems. This research overcomes the difficulties of forecasting that arise when considering future information. In this paper, we seek to find the minimum amount of information (effective information) necessary to evaluating system performance offline or to optimally control a system. The existence of effective information is proved and a methodology for determining it is developed. It is also shown that ignoring information beyond the planning horizon leads to significant performance loss and may even lead to violating the constraints of a control problem.

Understanding probabilistic risk in predisposition genetic testing for Alzheimer disease.

The utility of genetic testing in preventing the onset of conditions such as Alzheimer disease (AD) depends upon categorizing individuals based on their "risk" of illness. Although no strategies to prevent the occurrence of AD have been proven effective, the promise of the new molecular medicine is based on the assumption that those "at risk" can be identified, counseled about their likelihood of developing a disease, and prescribed specific preventive interventions. We suggest that this paradigm of disease prevention through risk stratification has limitations that have not been fully explored. Within the fields of cognitive and health psychology, research has addressed how individuals understand the numeric presentation of uncertain future events; this research often focuses on the "perceptual pathology" of lay people. The uncertainty inherent in the risk estimates themselves is rarely considered, nor has research addressed how those risk estimates are created. There has been limited work to date focused on genetic risk assessment, and little is known about how individual might understand the meaning or consequences of being at genetic risk for AD. An emerging social science critique of concepts of risk in genetic medicine offers a broader perspective, examining the process of producing scientific risk estimates as well as the response of individual patients, such as the experience of embodied risk that is central to genetic testing. Making fully informed choices about genetic testing for AD may prove more difficult than our current model of ethical practice suggests.

Strategic facility location: A review

Facility location decisions are a critical element in strategic planning for a wide range of private and public firms. The ramifications of siting facilities are broadly based and long-lasting, impacting numerous operational and logistical decisions. High costs associated with property acquisition and facility construction make facility location or relocation projects long-term investments. To make such undertakings profitable, firms plan for new facilities to remain in place and in operation for an extended time period. Thus, decision makers must select sites that will not simply perform well according to the current system state, but that will continue to be profitable for the facility's lifetime, even as environmental factors change, populations shift, and market trends evolve. Finding robust facility locations is thus a difficult task, demanding that decision makers account for uncertain future events. The complexity of this problem has limited much of the facility location literature to simplified static and deterministic models. Although a few researchers initiated the study of stochastic and dynamic aspects of facility location many years ago, most of the research dedicated to these issues has been published in recent years. In this review, we report on literature which explicitly addresses the strategic nature of facility location problems by considering either stochastic or dynamic problem characteristics. Dynamic formulations focus on the difficult timing issues involved in locating a facility (or facilities) over an extended horizon. Stochastic formulations attempt to capture the uncertainty in problem input parameters such as forecast demand or distance values. The stochastic literature is divided into two classes: that which explicitly considers the probability distribution of uncertain parameters, and that which captures uncertainty through scenario planning. A wide range of model formulations and solution approaches are discussed, with applications ranging across numerous industries.

Examining Changes in Reserves Using Stochastic Interest Models

Introduction On balance sheets, assets must equal liabilities plus net worth of the firm. An important component of these liabilities for insurers is the reserve, the portion of a firms' assets set aside to meet future uncertain obligations arising from insurance contracts. Although the obligations of each contract are contingent upon uncertain future events, and thus may be modeled stochastically, the reserve set aside is a single number. There are limitations when using a single number to summarize a stochastic quantity. However, reserves play a prominent role in financial statements and thus these quantities are important to managers of insurance organizations. Several important problems in actuarial science rely heavily on the determination of a reserve. To illustrate, if a company or a block of business is to be traded on the open market, a must be determined for the associated set of obligations. Thus, it is useful to think of a reserve as the value associated with a set of stochastic obligations. As another application, reserves traditionally have been used as a measure of an organization's financial strength. In this context, the reserve should be larger than the value of obligations, because a conservative approach should be taken for assessing potential future obligations. Life insurance and annuity reserves are calculated by summarizing discounted cash flows, where the discounting is done with respect to investment earnings, as well as decrements due to mortality, disability, policy lapse, and so on, that may be applicable to a particular policy. For brevity, this articles considers only investment earnings and the mortality decrement. Extensions to the multidecrement case are straightforward. In the traditional insurance literature, as in Jordan (1967), the deterministic assumption dominates the development of the theory of life contingencies. Namely, mortality occurs according to a known mortality table and the interest rate is assumed to have a deterministic value. One step further is to allow the age at death to be a random variable, although the interest rate is assumed to be deterministic--a approach followed in Bowers et al. (1986). Insurance literature has generalized the traditional theory of life contingencies by introducing stochastic variation in interest rates (Boyle, 1976; Waters, 1978; Panjer and Bellhouse, 1980; Bellhouse and Panjer, 1981; Giaccotto, 1986; Dhaene, 1989; Frees, 1990; and Beekman and Fuelling, 1990, 1991). Additional stochastic interest models from financial economics are discussed below. This article computes reserves as (conditional) expectations of sums of future cash flows. Motivation for this approach can be found in, for example, Bowers et al. (1986) for the semi-stochastic approach and Buhlmann (1992) for models using stochastic interest. Here, we are primarily concerned with quantifying changes in reserves from one financial period to the next. Changes in reserves could be used to quantify the amount of profit released, as in Ramlau-Hansen (1988), which studied gains and losses emerging from margins built into mortality and other decrements and ignored those arising from investments. To complement that work, we focus on changes arising from stochastic interest rates and do not explicitly consider margins built into other decrement rates. Changes in of future obligations due to dynamic models of interest have been considered extensively in the financial economics literature, in particular as part of immunization theory, which deals with instantaneous changes in value. Here, we examine changes in from one financial period to the next. The new idea of examining changes in reserves can be illustrated by considering the following simple scenario. Let {[y.sub.s]} represent the random force of interest in the sth period. As argued in Frees (1990), [y.sub. …

Planning for Uncertainty: The Case of local Disaster Planning

Abstract Adoption of good disaster preparedness practices (practices) can mitigate recurrent problems in community disaster management. However, there are obstacles to adoption of such practices; these obstacles include lack of experience with disasters (experience) and lack of an effective planning process (planning). Since the uncertainty that results from insufficient experience is a given, while planning reflects an organizational choice, this study investigates whether effective planning compensates for lack of experience in promoting the adoption of good practices. Analyses of survey data reveal that jurisdictions with the most experience tend to have effective planning and to have adopted good practices. Among jurisdictions with little experience, those with effective planning are significantly more likely to have adopted good practices than those without it. Process-oriented planning activities, such as multidisciplinary simulations and task forces, are more effective than technical activities, such as writing standard procedures. Public agencies can therefore undertake planning activities that increase opportunities to prepare effectively for uncertain future events.

Scoring Rules for Probabilistic Reporting

The values of assets and liabilities frequently depend on uncertain future events, as well as measurement error and uncertain recording of past events. Hence, financial statements are essentially probabilistic in nature. It has often been suggested that users' needs would be better served if interval estimates or complete probability distributions were presented in published financial statements, rather than the traditional point estimates.1 Such suggestions are usually made in passing, however, and to date I have seen few frontal attacks on the justification, implications, and problems of preparing probabilistic financial statements.2 The purpose of this paper is to examine probabilistic reporting within a decision-theoretic framework. The paper consists of six parts. Section 2 shows that under ideal conditions, that is, knowledge by the auditor of a single user's decision problem and utility function, the presentation of probabilistic financial statement information is conceptually straightforward. Indeed, there is little to be gained from probabilistic financial statements under these conditions, since the auditor has enough knowledge to calculate an optimal point estimate financial statement. Given that ideal conditions are unlikely, it is then shown that a significant individual demand for probabilistic statement information will exist. Each user can insert the

Sensitivity Analysis of Douglas Equipment Life Model

The Douglas Model is a complex mathematical model used to determine the optimal economic life of construction equipment. It has a large number of input parameters. Many of the input parameters are based on predictions of uncertain future events. Monte Carlo simulation was used to study the sensitivity of the Douglas Model to random errors in the determination of the input parameters. The uncertainty of the parameters was assumed to be described by normal and beta probability distributions, and 100 simulations were made for each of the assumed distributions. It is concluded that the Douglas Model is not too sensitive to random variation of the input parameters.

A Model for Selecting Commercial Bank Government Security Portfolios

T HE object of this paper is to formulate a normative model for selecting a bank's Government security portfolio. Two major problems arise in constructing a model of bank portfolio selection. First, the model must handle uncertainty. This includes not only uncertain future events but also the decision maker's preferences for the outcomes associated with these events. Second, it must recognize the intertemporal or multi-period character of the decision making process. This means that a decision made in one period will influence subsequent decisions and hence, that subsequent decisions must be considered in arriving at the present one. The present paper applies Bayesian and sequential decision theory to handle both the expectationally stochastic and the dynamic aspects of this important decision problem simultaneously and consistently. No previous model of commercial bank portfolio selection handles either or both problems satisfactorily. Porter's model of bank asset selection recognizes uncertainty by treating future cash flows and security prices as random variables, but it is only one period in length. Moreover, it does not consider the decision maker's preferences.' Since the objective function is linear, the model produces a portfolio diversified between securities and loans only through the selection of distribution functions describing the random variables. These transform the function into a nonlinear one upon integration. Cheng's model of bank security portfolio selection is, in effect, a one period formulation also.2 It incorporates uncertainty and the decision maker's preferences through Markowitz's efficient portfolio concept.3 An efficient portfolio is one which maximizes expected return for a given variance of return (or minimizes the variance of return for a given expected return). As Tobin points out, however, this criterion assumes, quite restrictively, that either the variable return is normally distributed or that the decision maker has a quadratic utility function.4 Cheng also makes the highly unrealistic assumption that securities are held to maturity. Multi-period bank portfolio selection models are all based on the assumption that future events are known with certainty. One such model formulated by Chambers and Charnes attempts to reflect the risk inherent in different portfolio configurations by including the Federal Reserve's capital adequacy formula as a constraint.5 Used in the supervision of banks, the capital adequacy formula allocates a bank's capital to designated asset categories on a fractional basis. The values of the fractions are designed to measure the percent by which the different asset categories would decline in market value if they had to be liquidated quickly.6 The choice of these values is somewhat arbitrary. Moreover, the formula itself implicitly assumes a particular preference structure and a certain probabilistic occurrence of future events. Neither assumption is likely to represent accurately either the decision maker's preferences or expectations.7

The Gains from Basing Grain Storage Decisions on Cash-Future Spreads

TRADITIONALLY, hedging by holding a short position in a futures contract has been viewed as a means of reducing the price risks associated with storage. However, Holbrook Working [5, pp. 436 ff.] and others have pointed out that firms hedge for other purposes besides simply avoiding risks. Our objective in this study was to identify and measure some of the potential benefits from hedging beyond simple risk reduction. Clearly, the need to predict uncertain future events is a major factor in grain storage decisions. Storing grains is like participating in a game of chance where the odds change each time the game is played. Hedging does not usually change the odds, but the hedger may be able to estimate the odds more accurately than the nonhedger. If so, not only can he avoid risk, but he may actually increase his average earnings over the long run by refusing to play the game, i.e., by refusing to store grains, on those occasions when earning prospects are particularly unfavorable. In most situations, hedging has little effect on average storage earnings when grains are held over the same intervals each year.1 If a manager is to increase his grain storage earnings through hedging, he must be able to predict year-to-year differences in storage earnings on hedged grains and he must respond to these predictions by altering his storage operations from year to year. In this study, least-squares regression was employed to determine whether cash-future spreads could be used effectively to forecast year-to-year differences in corn price movements over the storage season in Michigan. The potential gains from employing such predictions were estimated by incorporating the forecasting equations into decision rules and simulating their application using a Monte Carlo procedure.2