Contributions in Merlin

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We draw on group information-processing theory to investigate how target boards of directors may contribute to target value capture during the private negotiations phase in acquisitions. We view target boards as information-processing groups and private negotiations as information-processing tasks. We argue that target board meeting frequency is associated with increased processing—gathering, sharing, and analyzing—of acquisition-related information, which improves target bargaining and, ultimately, target value capture. We further posit that this value-enhancing effect of target board meeting frequency is more pronounced when target board composition improves the ability of target boards to process acquisition-related information. Finally, we expect that meeting frequency is more consequential for target bargaining and value capture when acquisition complexity imposes high information-processing demands on the target boards during private negotiations. Empirical evidence from a sample of acquisitions of publicly listed firms in the United States offers support for our group information-processing perspective on board contribution to shareholder value in firms targeted for acquisition.

Over the course of the past two decades, it has become a common experience for consumers authorizing an international transaction via credit card to be invited to choose the currency in which they wish the transaction to be executed. While this choice, made feasible by a technology known as dynamic currency conversion (DCC) , seems to foster competition, we argue that the opposite is the case. In fact, the unique pure-strategy equilibrium in a natural fee-setting game, with uninformed and possibly inattentive consumers, turns out to be highly asymmetric, entailing fees for the service provider that persistently exceed the monopoly level. Although losses in welfare may be substantial, a regulatory solution is unlikely to come about due to a global free-rider problem.

Decisions in management and finance rely on information that often includes win-lose feedback (e.g., gains and losses, success and failure). Simple reinforcement then suggests to blindly repeat choices if they led to success in the past and change them otherwise, which might conflict with Bayesian updating of beliefs. We use finite mixture models and hidden Markov models, adapted from machine learning, to uncover behavioral heterogeneity in the reliance on difference behavioral rules across and within individuals in a belief-updating experiment. Most decision makers rely both on Bayesian updating and reinforcement. Paradoxically, an increase in incentives increases the reliance on reinforcement because the win-lose cues become more salient.

Comparing banks to non-bank lenders, we investigate whether the geographical distance between lenders, borrowers, and their properties is reflected in the pricing of US mortgages that were included in US commercial mortgage-backed security (CMBS) pools during the 2000 to 2017 period. The difference in loan spreads when the bank-borrower distance increases from zero to the median of about 700 miles is 10 basis points, and this effect is more pronounced if the loan is collateralized by a riskier property. On the contrary, geographical distance does not seem to have any effect on the loan spread of mortgages granted by non-bank lenders. The difference in loan pricing across originator types (even after controlling for key mortgage and property characteristics) suggests that banks and non-bank lenders have different incentives, lending technologies, and/or different types of borrowers. Our results contribute to the emerging literature on non-bank lender behavior.

Humans are generally risk averse, preferring smaller certain over larger uncertain outcomes. Economic theories usually explain this by assuming concave utility functions. Here, we provide evidence that risk aversion can also arise from relative underestimation of larger monetary payoffs, a perceptual bias rooted in the noisy logarithmic coding of numerical magnitudes. We confirmed this with psychophysics and functional magnetic resonance imaging, by measuring behavioural and neural acuity of magnitude representations during a magnitude perception task and relating these measures to risk attitudes during separate risky financial decisions. Computational modelling indicated that participants use similar mental magnitude representations in both tasks, with correlated precision across perceptual and risky choices. Participants with more precise magnitude representations in parietal cortex showed less variable behaviour and less risk aversion. Our results highlight that at least some individual characteristics of economic behaviour can reflect capacity limitations in perceptual processing rather than processes that assign subjective values to monetary outcomes.

We consider dynamic stochastic economies with heterogeneous agents and introduce the concept of uniformly self-justified equilibria (USJE)-temporary equilibria for which expectations satisfy the following rationality requirements: i) individuals' forecasting functions for the next period's endogenous variables are assumed to lie in a compact, finite-dimensional set of functions, and ii) the forecasts constitute the best uniform approximation to a selection of the equilibrium correspondence. We show that in contrast to rational expectations equilibria, USJE always exist, and we develop a simple algorithm to compute them. As an application, we discuss a stochastic overlapping generations exchange economy. We give an example where recursive (rational expectations) equilibria fail to exist and explain how to construct USJE for that example. In addition, we provide numerical examples to illustrate our computational method.

Cyber-Angriffe beschäftigen die Schweizer Wirtschaft mehr denn je. Jedes zweite Grossunternehmen wurde bereits Opfer eines Cyber-Angriffs. In vielen Fällen ist die Folge ein Betriebsunterbruch. Die 14. Ausgabe des swissVR Monitors zeigt: Obwohl das Bewusstsein für die Risiken zunimmt, fehlt vielen Firmen eine klar formulierte Cyber-Strategie. Der Ernstfall wird nur selten geprobt und auch das Reporting der Geschäftsleitung an den Verwaltungsrat muss sich verbessern.

We consider the “block withholding attack” as introduced by Eyal, where mining pools may infiltrate others to decrease their revenues. However, when two mining pools attack each other and neither controls a strict majority, the so-called miner’s dilemma arises. Both pools are worse off than without an attack. Knowing this, pools may make implicit non-attack agreements. Having said this, the miner’s dilemma is known to emerge only if no pool controls the majority of the mining power. In this work, we allow for miner migration and show that the miner’s dilemma emerges even for pools whose mining power exceeds 50%. We construct a game, where two mining pools attack each other and use simulation analysis methods to analyze the evolution the pools’ mining power, infiltration preferences and revenue densities under the influence of different mining pool sizes and miner migration preferences. The results show that underlying game experiences a phase transition fueled by miners’ migration preference. Without migration, it is profitable for a large mining pool to attack the other pool. The higher the migration preference of the miners, the more the game transitions into the miner’s dilemma and attacking makes both pools worse off. In a second step, we introduce solo-mining into the system. Introducing solo-mining cannot prevent the miner’s dilemma, however, it improves the efficiency of the mining process as the infiltration preferences of the mining pools are lowered. Thus, solo-mining has a control effect on the miner’s dilemma by keeping the infiltration preference below a certain threshold.

First-person view (FPV) drone racing is a televised sport in which professional competitors pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the perspective of their drone by means of video streamed from an onboard camera. Reaching the level of professional pilots with an autonomous drone is challenging because the robot needs to fly at its physical limits while estimating its speed and location in the circuit exclusively from onboard sensors. Here we introduce Swift, an autonomous system that can race physical vehicles at the level of the human world champions. The system combines deep reinforcement learning (RL) in simulation with data collected in the physical world. Swift competed against three human champions, including the world champions of two international leagues, in real-world head-to-head races. Swift won several races against each of the human champions and demonstrated the fastest recorded race time. This work represents a milestone for mobile robotics and machine intelligence, which may inspire the deployment of hybrid learning-based solutions in other physical systems.