Information Economics

Information economics can be best described as a shift in the traditional neoclassical assumption of perfect information. Neoclassical economics assumes that all actors have access to perfect information and are rational in their behavior. Over the years, as scholars have realized that the assumptions of neoclassical economics are not an accurate reflection of the real world, other research streams have developed that relax these assumptions. Information economics is one such stream, arguing that actors or parties have differential access to information, which raises the concern of adverse selection and moral hazard when the actors or parties participate in a transaction. Adverse selection occurs when one party has more information about the product or service than the other party and it leads to a less profitable or riskier transaction for the uninformed party. Alternatively, moral hazard occurs after the transaction, where one party has an incentive to engage in risky behavior when the other party bears the cost of failure. Information economics offers insights to both these concerns and offers solutions in the form of signaling and protection mechanisms. Signaling theory, a component of information economics, addresses how one party can credibly convey information to its potential exchange partners to facilitate transactions. The concepts of information asymmetry and signaling have been widely used in economics and business research to understand concepts ranging from game theoretic models of investments to principal–agent relationships to adverse selection problems in transactions. Information economics offers strong foundations for research within management as it helps understand several phenomena related to organizational transactions. For instance, corporate strategy scholars have utilized the predictions stemming from information economics in acquisition research to study target search, selection, signaling behavior, acquisition contracting, premiums, and governance. Information economics also has broad potential to affect firms’ organizational governance and entry mode choices. The following paragraphs will discuss how this theory has been developed and provide a few applications of information economics in strategy and management research.

Brand premia driven by perceived vertical differentiation in markets with information disparity and optimistic consumers

We have considered a duopoly with perceived vertical differentiation, information disparity and optimistic consumers. When firms compete for informed and uninformed consumers, the former contribute to raise product quality, while equilibrium prices increase with optimistic misperception of the latter, in our first equilibrium. Brand premium includes a quality premium and a misperception rent. In our second equilibrium, informed consumers buy low-quality goods and minimum product differentiation without Bertrand competition occurs. The brand premium is just a misperception rent, however, an increase of the informed consumers share implies price re-balancing and rent reduction. Consumers externalities arise in both equilibria. Firms compete only for informed consumers within our third and fourth equilibrium, as uninformed ones are passive and represent a captive market. Uninformed consumers in one case are overoptimistic, they buy the high quality good and can be cheated in equilibrium. Uninformed consumers approach the real quality differential in the fourth equilibrium, and the model reduces to standard vertical differentiation with perfect information.

Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information

The expected value of partial perfect information (EVPPI) provides an upper bound on the value of collecting further evidence on a set of inputs to a cost-effectiveness decision model. Standard Monte Carlo estimation of EVPPI is computationally expensive as it requires nested simulation. Alternatives based on regression approximations to the model have been developed but are not practicable when the number of uncertain parameters of interest is large and when parameter estimates are highly correlated. The error associated with the regression approximation is difficult to determine, while MC allows the bias and precision to be controlled. In this article, we explore the potential of quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) estimation to reduce the computational cost of estimating EVPPI by reducing the variance compared with MC while preserving accuracy. We also develop methods to apply QMC and MLMC to EVPPI, addressing particular challenges that arise where Markov chain Monte Carlo (MCMC) has been used to estimate input parameter distributions. We illustrate the methods using 2 examples: a simplified decision tree model for treatments for depression and a complex Markov model for treatments to prevent stroke in atrial fibrillation, both of which use MCMC inputs. We compare the performance of QMC and MLMC with MC and the approximation techniques of generalized additive model (GAM) regression, Gaussian process (GP) regression, and integrated nested Laplace approximations (INLA-GP). We found QMC and MLMC to offer substantial computational savings when parameter sets are large and correlated and when the EVPPI is large. We also found that GP and INLA-GP were biased in those situations, whereas GAM cannot estimate EVPPI for large parameter sets.

Multilevel and Quasi Monte Carlo methods for the calculation of the Expected Value of Partial Perfect Information

The expected value of partial perfect information (EVPPI) provides an upper bound on the value of collecting further evidence on a set of inputs to a cost-effectiveness decision model. Standard Monte Carlo (MC) estimation of EVPPI is computationally expensive as it requires nested simulation. Alternatives based on regression approximations to the model have been developed, but are not practicable when the number of uncertain parameters of interest is large and when parameter estimates are highly correlated. The error associated with the regression approximation is difficult to determine, while MC allows the bias and precision to be controlled. In this paper, we explore the potential of Quasi Monte-Carlo (QMC) and Multilevel Monte-Carlo (MLMC) estimation to reduce computational cost of estimating EVPPI by reducing the variance compared with MC, while preserving accuracy. In this paper, we develop methods to apply QMC and MLMC to EVPPI, addressing particular challenges that arise where Markov Chain Monte Carlo (MCMC) has been used to estimate input parameter distributions. We illustrate the methods using a two examples: a simplified decision tree model for treatments for depression, and a complex Markov model for treatments to prevent stroke in atrial fibrillation, both of which use MCMC inputs. We compare the performance of QMC and MLMC with MC and the approximation techniques of Generalised Additive Model regression (GAM), Gaussian process regression (GP), and Integrated Nested Laplace Approximations (INLA-GP). We found QMC and MLMC to offer substantial computational savings when parameter sets are large and correlated, and when the EVPPI is large. We also find GP and INLA-GP to be biased in those situations, while GAM cannot estimate EVPPI for large parameter sets.

Optimal guessing in ‘Guess Who’

Are you Richard? Are you Anne? We look at the strategic problem in the children’s guessing game Guess Who, which is a form of zero-sum symmetric game with perfect information. We discuss some preliminary strategic insights and formally derive an optimal strategy and win-probabilities for the game. We discuss the first-mover advantage in the game and other strategic aspects coming out of the optimal strategy. While the paper is based on the popular children’s game, our analysis generalises the actual game by allowing any initial game state with an arbitrarily large number of starting characters. With the aid of these mathematical results you can now comprehensively thrash your young children and be a terrible parent!

Informácia o jednej zaujímavej všeobecno-vzdelávacej aktivite časopisu Quark

At the end of 2020, the Quark editorial office launched on its Facebook page an interesting competition called "Photo Puzzle". The basic premise is that the editors of the journal publish an original picture related in some way to science and technology, with an enclosed question. However, the image itself usually does not provide enough information to identify the correct answer. Instead, it provides an opportunity to creatively think about possible solutions in the absence of perfect information. In this sense, the questions differ from the typical school problems, which require accurate formulation and have clear answers. Since in everyday life we rarely encounter this type of „school“ questions, this activity by Quark staff should be appreciated.

Subgame‐perfect equilibrium in games with almost perfect information: Dispensing with public randomization

Harris, Reny, and Robson (1995) added a public randomization device to dynamic games with almost perfect information to ensure existence of subgame perfect equilibria (SPE). We show that when Nature's moves are atomless in the original game, public randomization does not enlarge the set of SPE payoffs: any SPE obtained using public randomization can be “decorrelated” to produce a payoff‐equivalent SPE of the original game. As a corollary, we provide an alternative route to a result of He and Sun (2020) on existence of SPE without public randomization, which in turn yields equilibrium existence for stochastic games with weakly continuous state transitions.