Implicit in the drug-approval process is a trade-off between Type I and Type II error. We propose using Bayesian decision analysis (BDA) to minimize the expected cost of drug approval, where relative costs are calibrated using U.S. Burden of Disease Study 2010 data. The results for conventional fixed-sample randomized clinical-trial designs suggest that for terminal illnesses with no existing therapies such as pancreatic cancer, the standard threshold of 2.5% is too conservative; the BDA-optimal threshold is 27.9%. However, for relatively less deadly conditions such as prostate cancer, 2.5% may be too risk-tolerant or aggressive; the BDA-optimal threshold is 1.2%. We compute BDA-optimal sizes for 25 of the most lethal diseases and show how a BDA-informed approval process can incorporate all stakeholders’ views in a systematic, transparent, internally consistent, and repeatable manner.

One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility. Also known as the “leverage effect”, this relationship was first documented by Black (1976), who attributed it to the effects of financial or operating leverage. This paper documents that firms which had no debt (and thus no financial leverage) from January 1973 to December 2017 exhibit Black’s leverage effect. Moreover, it finds that the leverage effect of firms in this sample is not driven by operating leverage. On the contrary, in this sample the leverage effect is stronger for firms with low operating leverage as compared to those with high operating leverage. Interestingly, the firms with no debt from the lowest quintile of operating leverage exhibit the leverage effect that is on par with or stronger than that of debt-financed firms.

In this article, the authors run experiments to test if and how human subjects can differentiate time series of actual asset returns from time series that are generated synthetically via various processes, including AR1. In contrast with previous anecdotal evidence, they find that subjects can distinguish between the two. These results show that temporal charts of asset prices convey to investors information that cannot be reproduced by summary statistics. They also provide a first refutation based on human perception of a strong form of the efficient-market hypothesis. Their experiments are implemented via an online video game (http://arora.ccs.neu.edu). The authors also link the subjects’ performance to statistical properties of the data and investigate whether subjects improve performance while playing.

Opinion article by Andrew W. Lo and Kenneth Kosik on the potential role of California in the space of Alzheimer's Disease research.

This two-volume set brings together a unique collection of key publications at the intersection of biology and economics, two disciplines that share a common subject: Homo sapiens. Beginning with Thomas Malthus–whose dire predictions of mass starvation due to population growth influenced Charles Darwin–economists have routinely used biological arguments in their models and methods. This collection summarizes the most important of these developments, including articles in sociobiology, evolutionary psychology, behavioral ecology, behavioral economics and finance, neuroeconomics, and behavioral genomics. Together with an original introduction by the editors, this important research collection will appeal to economists, biologists, and practitioners looking to develop a deeper understanding of the limits of Homo Economicus.

It is a rare pleasure and honor for a former undergraduate student in Martin Shubik’s popular game theory classes at Yale University to be asked to write a review of his professor’s latest book, The Guidance of an Enterprise Economy, published by MIT Press in 2016. In contrast to the old saw in which “the student is now the master,” this volume confirms that the student is still the student and the master is—and always will be—the master.

Shubik, the world-renowned game theorist, and his co-author, Eric Smith, an impressive physicist cum biologist cum economist at the Santa Fe Institute, have undertaken an ambitious agenda to formulate a grand synthesis of the different levels of economic theory—financial, microeconomic, organizational, and macroeconomic—and reintroduce dynamics within the framework of general equilibrium (GE). They have written a fascinating, provocative, and occasionally frustrating volume that moves a much-neglected topic forward.

Capitalism is a powerful tool: By compressing enormous amounts of information regarding supply and demand into a single number—the market price—buyers and sellers are able to make remarkably intelligent decisions simply by engaging in self-interested behavior. But in a big-data world, where a supercomputer can fit into our pocket and a simple Internet search allows us to find every product under the Sun, do we still need it?

In Reinventing Capitalism in the Age of Big Data, Viktor Mayer-Schönberger and Thomas Ramge argue that big data will transform our economies on a fundamental level. Money will become obsolete, they argue, replaced by metadata. Instead of a single market price for each commodity, sophisticated matching algorithms will use a bundle of specifications and personal preferences to select just the right product for you. Artificial intelligence powered by machine-learning techniques will relentlessly negotiate the best possible transaction on your behalf. Capital will still be important, they concede, but increasingly just for its signaling content. “Venture informers” might even replace venture capitalists.

The tenth anniversary of the disastrous weekend that nearly brought down the global financial system is fast approaching. But in many of the jurisdictions that were central to the crisis, financial regulations introduced in the aftermath, aimed at preventing a repeat, are now being rolled back. The pendulum of regulation is now swinging back towards fewer and looser restrictions – and if the past is any guide, a ramp-up in systemic risk exposures will be the result.

‘Fintech’ is transforming the financial sector at a pace that is now obvious even to the casual observer. We see this not only in daily headlines about initial coin offerings or financial applications of blockchain technology, but also in the daily experiences of the average consumer: paper cheques consigned forever to desk drawers, automatic currency conversions on a trip abroad, the rapid approval of an online loan – and most excitingly for some, personal investing.

August is typically when Wall Street goes to the beach, the mountains, or just home to recharge for a week or two. Many Europeans take the entire month off. But financial markets have a cruel knack of ruining holidays. As we lie in our hammocks this August, we might do well to recall a remarkable event that occurred, seemingly without warning, 11 years ago this month in the run-up to the financial crisis.

The increasing dominance of technology in daily lives is finally penetrating the financial industry as well. The growing popularity of algorithmic trading, mobile payment platforms and robo-advisers is just the beginning of the fintech revolution. But perhaps the most radical - and controversial - innovation in today's headlines is cryptocurrencies. Extreme volatility makes products an unreliable store of value - for now.

The information revolution has transformed everyday life for billions of people throughout the world. For example, according to mobile phone research group GSMA Intelligence, there are currently over 5 billion unique mobile phone subscribers, out of an estimated global population of 7.6 billion. This is the equivalent of a mobile phone for every person on the planet between the ages of 15 and 65.

In the past decade, financial industry excesses have been cited as the source of many ills afflicting economies and political systems in the West. But, if used responsibly, finance could help provide the cure for some of humanity’s most pressing problems – from cancer to fossil fuel depletion and climate change.

Bacteria are easily characterizable model organisms with an impressively complicated set of abilities. Among them is quorum sensing, a cell-cell signaling system that may have a common evolutionary origin with eukaryotic cell-cell signaling. The two systems are behaviorally similar, but quorum sensing in bacteria is more easily studied in depth than cell-cell signaling in eukaryotes. Because of this comparative ease of study, bacterial dynamics are also more suited to direct interpretation than eukaryotic dynamics, e.g., those of the neuron. Here we review literature on neuron-like qualities of bacterial colonies and biofilms, including ion-based and hormonal signaling, and a phenomenon similar to the graded action potential. This suggests that bacteria could be used to help create more accurate and detailed biological models in neuroscientific research. More speculatively, bacterial systems may be considered an analog for neurons in biologically based computational research, allowing models to better harness the tremendous ability of biological organisms to process information and make decisions.

The debate between rational models of behavior and their systematic deviations, often referred to as “irrational behavior”, has attracted an enormous amount of research. Here, we reconcile the debate by proposing an evolutionary explanation for irrational behavior. In the context of a simple binary choice model, we show that irrational behaviors are necessary for evolution in stochastic environments. Furthermore, there is an optimal degree of irrationality in the population depending on the degree of environmental randomness. In this process, mutation provides the important link between rational and irrational behaviors, and hence the variety in evolution. Our results yield widespread implications for financial markets, corporate behavior, and disciplines beyond finance.