Optimal Allocation Strategies for the Dark Pool Problem

We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem to adversarial scenarios, while also improving on their results in the iid setup. The resulting algorithms are efficient, and perform well in simulations under stochastic and adversarial inputs

Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing

Motivated by practical considerations in machine learning for financial decision-making, such as risk-aversion and large action space, we initiate the study of risk-aware linear bandits. Specifically, we consider regret minimization under the mean-variance measure when facing a set of actions whose rewards can be expressed as linear functions of (initially) unknown parameters. Driven by the variance-minimizing G-optimal design, we propose the Risk-Aware Explore-then-Commit (RISE) algorithm and the Risk-Aware Successive Elimination (RISE++) algorithm. Then, we rigorously analyze their regret upper bounds to show that, by leveraging the linear structure, the algorithms can dramatically reduce the regret when compared to existing methods. Finally, we demonstrate the performance of the algorithms by conducting extensive numerical experiments in a synthetic smart order routing setup. Our results show that both RISE and RISE++ can outperform the competing methods, especially in complex decision-making scenarios

Poland on the dole: unemployment benefits, training, and long-term unemployment during transition

We analyse the duration of unemployment spells in Poland using data from the Polish Labour Force Survey of August 1994. The effects on the duration of unemployment of important socio-economic and demographic characteristics are explored besides the impacts of the unemployment benefit system and training schemes. Finally, we investigate whether prior unemployment influences one's chances to find a job. Entitlements to unemployment benefits prolong unemployment spell durations significantly. This effect is roughly of the same magnitude under the two benefit regimes that existed between 1990 and 1994, although the generosity of the unemployment benefit system has been reduced drastically in 1992. The results give credence to the view that the unlimited entitlement period of the old regime was not the main culprit for the widespread incidence of long-term unemployment. Training programmes organised by labour offices should not be regarded as a panacea for the problems of the long-term unemployed. The results suggest that active labour market policies should perhaps be seen more as a tool for social rather than economic policy. People with previous unemployment spells must expect to stay unemployed far longer than people who become unemployed for the first time. On the other hand, controlling for unobserved individual heterogeneity, we find that the probability of finding a job increases, especially for men, with the duration of unemployment. --unemployment duration,incentives,training,Poland,transition

Learning Best Response Strategies for Agents in Ad Exchanges

Ad exchanges are widely used in platforms for online display advertising. Autonomous agents operating in these exchanges must learn policies for interacting profitably with a diverse, continually changing, but unknown market. We consider this problem from the perspective of a publisher, strategically interacting with an advertiser through a posted price mechanism. The learning problem for this agent is made difficult by the fact that information is censored, i.e., the publisher knows if an impression is sold but no other quantitative information. We address this problem using the Harsanyi-Bellman Ad Hoc Coordination (HBA) algorithm, which conceptualises this interaction in terms of a Stochastic Bayesian Game and arrives at optimal actions by best responding with respect to probabilistic beliefs maintained over a candidate set of opponent behaviour profiles. We adapt and apply HBA to the censored information setting of ad exchanges. Also, addressing the case of stochastic opponents, we devise a strategy based on a Kaplan-Meier estimator for opponent modelling. We evaluate the proposed method using simulations wherein we show that HBA-KM achieves substantially better competitive ratio and lower variance of return than baselines, including a Q-learning agent and a UCB-based online learning agent, and comparable to the offline optimal algorithm

The Irrational Element of Self and Creation in the Time of the Plague

In this paper I discuss how I went beyond commonplace, rational ways of theater-making and relied on certain “extreme”, irrational gestures to create my production of Charles Mee’s Orestes 2.0. I discuss the circumstances that led me to unlock my subjective artistry, the manner in which I tackled and fulfilled my “directorial concept”, and how I created a production that challenged the tyranny of rationality both on the stage and within the culture of the theater department. I relate personal experiences entering school during a time of national suspicion, and I discuss how a more expansive artistic outlook developed in response to my environment. I go through the execution of my directorial concept and show how I “projected a world” from my interior into the theatrical concrete, drawing on the work of master Polish director Tadeusz Kantor. I describe the “rules” of my theatrical world in terms of its diegetic reality, its method of construction, and its aims. I then describe the rehearsal process, highlighting the ways that irrational methods and a focus on body and imagination drove the process. I discuss my creative state of mind, my performance as the character Farley, and the way in which I hoped authority and sense-making functioned in the audience experience of the performance.Throughout, I accompany my ideas with supporting quotations from Mee’s play and the writing of French theorist, poet, and director Antonin Artaud, situating my use of the power of the irrational inside the theatrical tradition and the play-text

Learning Valuation Distributions from Partial Observation

Auction theory traditionally assumes that bidders' valuation distributions are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson auction. However, this theory does not describe how the auctioneer comes to possess this information. Recently, Cole and Roughgarden [2014] showed that an approximation based on a finite sample of independent draws from each bidder's distribution is sufficient to produce a near-optimal auction. In this work, we consider the problem of learning bidders' valuation distributions from much weaker forms of observations. Specifically, we consider a setting where there is a repeated, sealed-bid auction with $n$ bidders, but all we observe for each round is who won, but not how much they bid or paid. We can also participate (i.e., submit a bid) ourselves, and observe when we win. From this information, our goal is to (approximately) recover the inherently recoverable part of the underlying bid distributions. We also consider extensions where different subsets of bidders participate in each round, and where bidders' valuations have a common-value component added to their independent private values