Multiple-objective methods in regulatory decision making: A conceptual framework applied to U.S. automobile safety standards

Public policy making is a complex matter. Policy makers are charged with balancing a broad spectrum of competing objectives, reflecting in varying degrees the interests and aspirations of a diverse range of constituencies and stakeholders. Policy decisions have differential impacts on differing constituencies and the contributions of these impacts to objectives are frequently uncertain, difficult to quantify, and hotly disputed. Formal methods of decision analysis have been advanced as aids for coping with complexity and have been applied to some public policy issues, most notably the management of water resources. While formal methods have the apparent benefit of rationalizing the policy process and improving the efficacy and equity of policy decisions, serious questions remain concerning the acceptability and ultimate usefulness of formal analyses in the public arena. In this article we examine these questions in the context of policy making relating to government regulation of automobile designs for safety. We consider what would be involved in attempting to use a relatively modern, multiple-objective approach in this context. The key questions are how and, more importantly, why multiple-objective methods might be used. The article begins with an illustrative case study, describes the current policymaking process, identifies the parties involved in and affected by this process, elicits key objectives, looks briefly at some scalar-objective approaches, and then outlines a multiple-objective approach. The framework developed arguably is useful in assisting policy making, at least at a qualitative level. Issues which impede a more quantitative resolution of this framework are discussed. This article is intended as a pilot study which may promote research toward the achievement of a useable multiple-objective procedure applicable in the public domain.     

Differences and similarities between ecological and economic models for biodiversity conservation

In this paper we investigate an important obstacle which substantially complicates co-operation between ecologists and economists but which has received little attention so far: differences between the modelling approaches in economics and ecology. To understand these differences, 60 models addressing issues relevant to biodiversity conservation have been selected randomly from eight international economic and ecological journals. The models have been compared according to a number of criteria including the models' level of generality; the mathematical techniques employed for formulation and solution of the models; the level of complexity and the way time, space and uncertainty are taken into account. The economic models sampled are formulated and analysed analytically, tend to be relatively simple and are mostly used to investigate general questions. Furthermore, they often ignore space, dynamics and uncertainty. Although some ecological models have similar properties, there is also a substantial number of another type of ecological models that are relatively complex and analysed by simulation. These models tend to be rather specific and often explicitly consider dynamics, space and uncertainty. The integrated ecological-economic models are observed to lie “in the middle” between ecological and economic models. An unexpected result is that they are not more complex than ecological and economic models (as one could have expected from a simple “merger” of models from both disciplines), but have an intermediate complexity.     

Thousands of BEERs: Take your pick

This paper explores the robustness of behavioral equilibrium exchange rate (BEER) models. We highlight the importance of model uncertainty, and employ real exchange rates computed from price‐level data to explore robustness to the inclusion of country fixed effects. The estimated coefficients—and therefore also the implied equilibrium values—are sensitive to the combination of variables included in the model, and to the inclusion of fixed effects. We identify several variables that exhibit a robust link with real exchange rates across specifications. Our findings can help policymakers in understanding the uncertainty associated with estimates of equilibrium exchange rates.     

Modeling Multiple-Objective Recreation Trips with Choices Over Trip Duration and Alternative Sites

Traditionally, recreation demand studies have focused on single-day, single-activity trips, despite anecdotal and empirical evidence that many recreational trips involve overnight stays and multiple purposes. This paper develops a random utility model that explores how visitors choose alternative sites and trip durations for multiple-objective trips. We focus on a recreational activity, beach visits, that appear to have significant proportions of the population taking single and multiple-day trips, and many of the multiple day trips involve multiple objectives. Multiple-duration and multiple-objective issues are incorporated in pricing trip costs. The results of the research suggest that the accepted method for incorporating travel costs into random utility models can lead to biased estimates of the structural utility parameters and, consequently, biased measures of welfare in a multiple-objective trip setting for single- and multiple-day users.     

Robust goal programming for multi-objective portfolio selection problem

This paper proposes a robust optimization model for the portfolio selection problem that uses a goal programming (GP) approach. In GP, decision makers can achieve more than one objective function. Some uncertain coefficients exist in both single and multi-objective models of the portfolio selection problem, which affects the feasibility and optimality of solutions. Robust optimization is an approach that deals with the uncertainty parameters in mathematical models, and guarantees the feasibility of the solutions. This paper tries to address the uncertainty parameters with robust optimization approach. This paper presents GP for the portfolio selection problem and addresses the uncertainty of the parameters by use of robust optimization approach. The approach is illustrated by a numerical example.     

Robustness and information processing

This paper considers a standard Kalman filtering problem subject to model uncertainty and information-processing constraints. It draws a connection between robust filtering [Hansen and Sargent, 2004. Robust control and economic model uncertainty. Monograph. In press] and Rational Inattention [Sims, 2003. Implications of rational inattention, Journal of Monetary Economics 50 (3), 665–690]. Considered separately, robustness and Rational Inattention are shown to be observationally equivalent, in the sense that a higher filter gain can either be interpreted as an increased preference for robustness, or an increased ability to process information. However, it is more interesting to consider them jointly. In this case, it is argued that an increased preference for robustness can be interpreted as an increased demand for information processing, while Sims' model of Rational Inattention can be interpreted as placing a constraint on the available supply. This suggests that the way agents actually implement robust decision rules is by allocating some of their scarce information processing capacity to problems that are characterized by high degrees of model uncertainty and risk-sensitivity.     

Environmental uncertainty,foresight and strategic decision making: An integrated study

This paper explores how the foresight practices and techniques that might be used for coping with environmental uncertainty are coordinated throughout large corporations and how their results are used for supporting strategic decision making. Based on an in-depth and multiple study of several companies, we analyse the main characteristics of environmental uncertainty - complexity and dynamism - that shape companies’ approaches to strategic foresight and uncertainty management. We explore the impact of growing complexity and dynamism on these approaches and the role of prediction and control in their design and implementation. We outline a conceptual framework for strategic foresight activities - and their relationships with decision making under uncertainty - as a planned learning process about the future which enables managers not to know opportunities and threats in advance, but to detect them more promptly and to react more effectively as soon as they start emerging.     

Dynamic modelling of agricultural policies: The role of expectation schemes

The highly disputed effects of agricultural trade liberalisation are mostly simulated with static models. Our main objective in this paper is to evaluate the robustness of the static simulation results to the consistent modelling of dynamic behaviours and to the linked specification of price/return expectations. Focusing on the scenario of a complete trade liberalisation of arable crop markets by developed countries, we find that available static results are quite robust compared to dynamic specifications and to most expectation schemes. Endogenous market fluctuations due to expectation errors may appear following trade liberalisation. These fluctuations are nevertheless limited by the many feedback effects revealed by our general equilibrium framework.     

The impact of uncertainty in a class of objective functions

This note analyzes some properties of a class of models involving optimization under uncertainty. Such models are characterized by a specific form of the objective function which is frequently encountered by economists. The discussion presented below generalizes a method which has been applied by several theoreticians [1–4, 8] to the analysis of specific topics in the theory of the firm and the problem of investment under uncertainty. This method makes use of a specific parameterization of risk and imposes some restrictions on decision makers' behavior, thus implying a model not as general as the one presented by Rothschild and Stiglitz [6, 7]. However, the present model is still plausible and affords some definite results where more general models are ambiguous.     

Robust monopoly pricing

We consider a robust version of the classic problem of optimal monopoly pricing with incomplete information. In the robust version, the seller faces model uncertainty and only knows that the true demand distribution is in the neighborhood of a given model distribution. We characterize the pricing policies under two distinct decision criteria with multiple priors: (i) maximin utility and (ii) minimax regret. The equilibrium price under either criterion is lower then in the absence of uncertainty. The concern for robustness leads the seller to concede a larger information rent to all buyers with values below the optimal price without uncertainty.     

Hayek, Keynes, and modern macroeconomics

The Great Recession seems to be creating a change in the trend of macroeconomic thinking. Prior to the financial crisis of 2008, dynamic stochastic general equilibrium (DSGE) models dominated the macroeconomics literature without any apparent challengers on the horizon. Since then, however, we have seen an increasing interest in macroeconomic models that address the state of confidence (??animal spirits??), complexity, cognition, and radical uncertainty. Most of the renewed interest in animal spirits, complexity, cognition, and radical uncertainty has come from a more or less ??Keynesian?? perspective. We discuss the potential to emphasize these elements from a more ??Hayekian?? perspective and argue that Austrian approaches to macroeconomics along these lines are more likely to resonate with mainstream economists than in years past.     

DEALING WITH UNCERTAINTY: ROBUST RULES IN MONETARY POLICY

We argue that in seeking to insure against model uncertainty, monetary policy makers are often ready to trade ex post performance for greater certainty in the outcome. They thus look for rules that although not optimal ex post, have certain properties that qualify them as robust. We apply first, Gul's approach of ‘disappointment’ aversion to describe policy makers' aversion to uncertainty and then define the properties the notion of ‘robustness’ entails. With these two tools we then link the desirability of such robust rules to the degree of policy makers' aversion to uncertainty. We thus show that provided such robust rules exist, a larger degree of disappointment aversion leads to a greater emphasis on robustness in policy implementation.     

Uncertainty and crude oil market volatility: new evidence

ABSTRACT

The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.     

经济政策不确定性、区域经济复杂度与企业创新行为

构建包含宏观经济政策不确定性指标、区域经济复杂度和上市公司特征变量“宏观-区域-企业”的实证面板数据，在理论分析经济政策不确定性对企业创新行为作用机理的基础上，以区域经济复杂度为视角，对经济政策不确定性与企业创新行为作用机制进行再检验。研究发现：经济政策不确定性对企业创新存在正向激励综合效应，同时，区域经济复杂度不仅能够促进企业创新行为，而且能够强化经济政策不确定性对企业创新行为的影响即抑制负向作用而强化正向作用。考虑内生性，通过替换核心变量检验稳健性以保证研究结论可靠，并且，研究企业特征发现，高科技企业和非高科技企业存在差异。     

Long-run growth empirics and new challenges for unified theory

Annual estimates of productivity are reported for periods over 500 years for eight countries and for five other countries over shorter periods. One- and two-break time series models are used to investigate discontinuities in productivity growth. The results support two-break models of long-run productivity and they favour approaches to unified growth modelling with three epochs. However, the lessening of productivity gaps and the decisive shifts to higher productivity occurred in the twentieth century, chiefly in the years around the World War II. The timing of the breaks and the complexity of the historical record highlights a need for unified models to connect more closely with economic history.     

Mixed forests and finance — Methodological approaches

Mixed forest ecosystems show effects of risk compensation similar to diversified portfolios of stocks. Other than portfolios of stocks, the composition of long-lived forests cannot be adjusted at short notice to current market developments. Thus, the financial robustness of tree-species portfolios is very important. However, it is still an open question, which financial approaches are appropriate to evaluate the financial robustness of mixed forests compared to single-species forests.This paper analysed various possibilities to assess portfolios of tree species, namely, the mean-variance (MV) approach applied for the selection of optimal portfolios, the second order stochastic dominance (SSD) criterion and the information-gap approach. For the numerical analysis existing financial data on Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica L.) were used which were generated by Monte-Carlo simulations (MCS) under risk.Regardless of the analysed approach, in all cases pure forests did not outperform mixed forests. The MV approach was restricted by the assumption of normally distributed financial returns. It had the tendency to favour larger proportions of the high-risk species Norway spruce. SSD was only able to distinguish between the efficient and the inefficient portfolios. It showed that forests with less than 20% (financial returns generated by MCS) or less than 30% (normally distributed financial returns) of Norway spruce were inefficient. Under information-gap modelling the advantages of risk compensation in mixed forests became clearest. Under the given assumptions, the financial robustness of a mixed forest (50% European beech and 50% Norway spruce) was between 4 and 18 times greater than that of pure Norway spruce.Modern information-gap modelling is very similar to the well known safety first rules and as such it is not that novel to financial optimisation as it seems at the first glance. Although the information-gap theory resulted in the same rankings as the minimisation of the estimated probability of failure, it nevertheless proved to be useful as a supplement to the other methods. Against the background of severe uncertainty inherent in long-term decision-making in forestry, information-gap modelling is based on appropriate assumptions. It favours a decision, which promises the greatest tolerance to possible errors in the available information, while still guaranteeing a minimal acceptable financial result. In principle, classical safety first rules do the same. However, the information-gap model is able to quantify the immunity of a decision against errors in the estimated financial data. From a theoretical point of view, this approach provides a supplementary and methodologically sound opportunity to incorporate severe uncertainty in decision-making.     

The impact of offering two versus three alternatives in choice modelling experiments

Researchers designing choice modelling experiments have some latitude over the number of choice alternatives that can be offered in each choice set. There is some evidence that design dimensions, including the number of alternatives available in each choice set, can influence model outcomes. A key issue is whether referendum formats with binary options are preferable to choice sets with multiple alternatives.A choice modelling experiment was performed where questionnaires delivered to two split samples differed only according to whether two or three alternatives were offered to respondents in each choice set. The results indicate that more robust models could be constructed from the three-alternative split compared to the two-alternative split. One reason for the difference is that respondents tended to display serial non-participation in the two-alternative format, choosing an alternative consistently without regard for changes in the attributes. For practitioners of the CM technique, the results suggest that it may be preferable to offer more than two alternatives in a choice set that includes a status quo option.     

Are estimated peer effects on smoking robust? Evidence from adolescent students in Spain

Adolescent smoking is one of the most pressing public health problems. The objective of this paper is to analyse the influence of peer pressure on adolescent cigarette consumption. More concretely, we explore the significance and robustness of the peer effects using several estimation methods employed in the existing literature. On the basis of the data provided by the 2004 Spanish survey on drug use in the school population, we estimate the probability of being a smoker by two-stage models. The results reveal that when we use standard errors used in the literature the class peer variable appears to be significant. However, the class peer variable is not significant when we calculate more exigent standard errors, a result that is robust across all specifications. The paper suggests the need for a more cautious interpretation of the peer effects found previously in the literature until a deeper analysis confirms the robustness of the peer effects.     

Determinants of the Effective Real Exchange Rate of the Synthetic Euro: Alternative Methodological Approaches

On the basis of historical data aggregated over the period 1973 to 2000, we have experimented with four different approaches to estimate the synthetic euro's equilibrium exchange rate. Using a number of competing models with the same data set, variable definitions and sample period offers the possibility to assess the uncertainty surrounding such equilibrium levels, both from an empirical (different estimates) and a theoretical viewpoint (different specifications). In this exercise, the 'Rest of the World' is proxied by the US, the UK, Japan and Switzerland, aggregated on the basis of trade weights.
We employed reduced form co–integration models, a structural VAR, a NATREX model (estimated in structural form) and the ECB's small–sized euro area wide macro–econometric model. In this order the approaches feature an increasing degree of 'structure', in the sense of the constraints based on economic theory embedded in the econometric models that were estimated. The results confirm the high likelihood for the euro having been undervalued in Q4 2000, while stressing the significant empirical and theoretical uncertainty with respect to the equilibrium exchange rate level.