Consumption Externalities and Diffusion in Pharmaceutical Markets: Antiulcer Drugs

We examine the role of consumption externalities in the demand for pharmaceuticals at both the brand level and over a therapeutic class of drugs. These effects emerge when use of a drug by others affects its value, and/or conveys information abut efficacy and safety to patients and physicians. This can affect that rate of market diffusion for a new entrant, and can lead to herb behavior whereby a particular drug can dominate the market despite the availability of close substitutes. We use data for H2-antagonist antiulcer drugs to estimate a dynamic demand model and quantify these effects. The model has three components: an hedonic price equation that measures how the aggregate usage of a drug, as well as conventional attributes, affect brand valuation; equations relating equilibrium market shares to quality-adjusted prices and marketing levels; and diffusion equations describing the dynamic adjustment process. We find that consumption externalities influence both valuations and rates of diffusion, but that they operate at the brand and not the therapeutic class level.

Averting catastrophes: the strange economics of Scylla and Charybdis

Faced with numerous potential catastrophes—nuclear and bioterrorism, megaviruses, climate change, and others—which should society attempt to avert? A policy to avert one catastrophe considered in isolation might be evaluated in cost-benefit terms. But because society faces multiple catastrophes, simple cost-benefit analysis fails: Even if the benefit of averting each one exceeds the cost, we should not necessarily avert them all. We explore the policy interdependence of catastrophic events, and develop a rule for determining which catastrophes should be averted and which should not

A Nonconvex Singular Stochastic Control Problem and its Related Optimal Stopping Boundaries

Abstract. Equivalences are known between problems of singular stochastic control (SSC) with convex performance criteria and related questions of optimal stopping; see, for example, Karatzas and Shreve [SIAM J. Control Optim., 22 (1984), pp. 856–877]. The aim of this paper is to inves-tigate how far connections of this type generalize to a nonconvex problem of purchasing electricity. Where the classical equivalence breaks down we provide alternative connections to optimal stopping problems. We consider a nonconvex infinite time horizon SSC problem whose state consists of an un-controlled diffusion representing a real-valued commodity price, and a controlled increasing bounded process representing an inventory. We analyze the geometry of the action and inaction regions by characterizing their (optimal) boundaries. Unlike the case of convex SSC problems we find that the optimal boundaries may be both reflecting and repelling and it is natural to interpret the problem as one of SSC with discretionary stopping

Unraveling the Historical Economies of Scale and Learning Effects for Desalination Technologies

As a technology develops and matures, both economies of scale and the lessons learned through experience drive down the cost over time. This article analyzes and separates the effects of economies of scale and learning through experience on historical cost reductions for three mature desalination technologies: multi‐effect distillation (MED), multi‐stage flash (MSF) distillation, and reverse osmosis (RO). The analysis suggests that learning has been the dominant driver for cost reductions, with learning rates of 23%, 30%, and 12% for MED, MSF, and RO, respectively, when the effects of scale are removed. The highest influence of economies of scale is found for MED, with an exponential scale coefficient of 0.71 and the largest difference between a traditional or scale‐free estimation of the learning rate. MSF and RO showed smaller differences between the traditional and de‐scaled learning rates (only 3%), pointing at learning as the main factor driving their historical cost reductions. However, a trend break observed over the last 10 years mirrors an exhaustion of the potential for technical improvements, as well as an increasing complexity and nonlinearity of the factors influencing the systems' cost. The findings provide useful data and insights for integrated and economic modeling frameworks, while providing guidance to prevent overestimations of the learning effect due to the confounding influence of economies of scale effects associated to historical unit upscaling processes

Detecting a Currency's Dominance or Dependence using Foreign Exchange Network Trees

In a system containing a large number of interacting stochastic processes, there will typically be many non-zero correlation coefficients. This makes it difficult to either visualize the system's inter-dependencies, or identify its dominant elements. Such a situation arises in Foreign Exchange (FX) which is the world's biggest market. Here we develop a network analysis of these correlations using Minimum Spanning Trees (MSTs). We show that not only do the MSTs provide a meaningful representation of the global FX dynamics, but they also enable one to determine momentarily dominant and dependent currencies. We find that information about a country's geographical ties emerges from the raw exchange-rate data. Most importantly from a trading perspective, we discuss how to infer which currencies are `in play' during a particular period of time

Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction $q(t)$ proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value $q_{max}$ if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future $r(t)$, dependent on their internal complexity. Here, we compare 'zero-intelligent' agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict $r(t)$. The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.Comment: 27 pp. v2 with minor corrections. See http://www.sg.ethz.ch for more inf