Policy Evaluation and Economic Policy Advice

Arguably, one of the most important developments in the field of applied economics during the last decades has been the emergence of systematic policy evaluation, with its distinct focus on the establishment of causality.By contrast to the natural sciences, the objects of our scientific interest typically exert some influence on their treatment status under the policy to be evaluated and on their economic outcomes. Thus, economic policy advice can only be successful, if it is based on an appropriate study design, experimental or observational. It will strive in societies that provide liberal access to data, accept the merits of randomized assignment and guard the independence of research institutions.Policy evaluation, applied economics, causality, policy advice

Decision Markets for Policy Advice

The main cause of bad policy decisions is arguably a lack of information. Decisionmakers often do not make use of relevant information about the consequences of the policies they choose. The problem, however, is not simply that public officials do not exploit readily available information. It is also that they do not take full advantage of creative mechanisms that could expand the supply of policy-relevant information. Among the most innovative and potentially useful information-generating mechanisms are speculative markets. Speculative markets produce public information about the perceived likelihood of future events as a natural byproduct of voluntary exchange. Speculative markets do a remarkable job of aggregating information; in every head-to-head field comparison made so far, their forecasts have been at least as accurate as those of competing institutions, such as official government estimates. Many organizations are now trying to take advantage of this effect, experimenting with the creation of "prediction markets" or "information markets," to forecast future events such as product sales and project completion dates. This chapter examines the uses and limitations of decision markets. Decision markets are information markets designed to inform a particular policy decision, by directly estimating relevant consequences of that decision. After reviewing the weaknesses of existing institutions, the mechanics of decision markets, and a concrete example, this chapter reviews the requirements, advantages, and disadvantages of decision markets. The chapter also takes a close look at a particular application of this tool: the controversial yet illuminating attempt to establish a "Policy Analysis Market" to forecast the consequences of major policy U.S. choices in the Middle East.

Policy Advice: Markets and Policies

This paper aims at the provision of applicable recommendations for institutions and actors involved regarding the EMU accession process both in CEE and in the eurozone. In order to provide topical advice, the first part, on markets, will concentrate on theory and empirics of labour markets, financial markets and foreign direct investment, whereas the second part, dealing with policies, will put emphasis on exchange rates, FDI, labour markets, and the social dimension. It turns out that benefits and losses of EMU accession may differ with regard to the different issue areas. To get to clear-cut recommendations, diverging impacts and their balance have been taken into consideration. Special regard has been given to divergent groups of winners and losers during accession, its impact on the political decisionmaking process, and ways to compensate for them.

Scientific Advice to Public Policy-Making

A feature of policy-making today is its dependence on scientific advice to deliver public policies that are robust, credible, and effective. This paper discusses how policy-making profits from scientific advice in areas where science and technology are significant. Particular attention is given to issues holding a high level of uncertainty, either because of inherent variability, because science is incomplete or controversial, or because data are inadequate to support a definitive answer. First, we analyse the social context that characterises the relationship between science and policy-making, with a focus on the decrease of public confidence in politicians and scientists. Second, we compare three different sets of guidelines on the collection and use of expertise in policy-making (issued by the UK, Canada and the European Commission, respectively) and identify two different approaches to scientific advice in policy-making. Third, based on a set of cross-national and multi-disciplinary case studies, we look at how the relationship between science and policy-making works in practice and propose a set of recommendations towards the establishment of a more robust and effective policy-making process.Scientific advice, Policy-making, Expertise

Policy Advice Derived from Simulation Models

When advising policy we face the fundamental problem that economic processes are uncertain. Consequently, policy can err. In this paper we show how the use of simulation models can reduce policy errors by inferring empirically reliable and meaningful statements about economic processes. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. By way of example we show what policy can learn with the help of abductive simulation models, namely how policy measures can influence the emergence of a regional cluster.Policy Advice, Simulation Models, Uncertainty, Methodology

Policy Advice Derived From Simulation Models

When advising policy we face the fundamental problem that economic processes are connected with uncertainty and thus policy can err. In this paper we show how the use of simulation models can reduce policy errors. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. This helps inferring empirically reliable and meaningful statements about how policy measures influence economic processes. By way of example we show how research subsidies by the government influence the likelihood that a regional cluster emerges.Policy Advice, Simulation Models, Uncertainty, Methodology

THE ROLE AND IMPORTANCE OF ACADEMIC FREEDOM IN THE POLICY PROCESS

Academic freedom, Policy process, policy decision-making, policy advice, research, research institute, Political Economy, Teaching/Communication/Extension/Profession,

Regret Bounds for Reinforcement Learning with Policy Advice

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand. We prove that RLPA has a sub-linear regret of \tilde O(\sqrt{T}) relative to the best input policy, and that both this regret and its computational complexity are independent of the size of the state and action space. Our empirical simulations support our theoretical analysis. This suggests RLPA may offer significant advantages in large domains where some prior good policies are provided