Time-Newsweek Cover Story Game

Dixit and Nalebuff (1991) provided a simple example of how Time and Newsweek compete with each other through their cover story decisions. This paper goes beyond this example to specify the conditions under which the two competing magazines (Time and Newsweek) issue the same or different cover stories. The main result of this paper can be described as follows. The difference in relative market power and relative market size of story A to story B are critical in determining the cover story decision (business strategy). If the market size of potential story A relative to story B is sufficiently large, then both magazines may issue the same cover story. However, if the market size of potential story A relative to story B is not large enough, the relative market power (rather than the relative market size) becomes more relevant and both magazines may issue different cover stories. This paper provides empirical evidence that supports our hypothesis and shows how our finding is related to Hotelling’s paradox

Toward a complete criterion for value of information in insoluble decision problems

In a decision problem, observations are said to be material if they must be taken into account to perform optimally. Decision problems have an underlying (graphical) causal structure, which may sometimes be used to evaluate certain observations as immaterial. For soluble graphs — ones where important past observations are remembered — there is a complete graphical criterion; one that rules out materiality whenever this can be done on the basis of the graphical structure alone. In this work, we analyse a proposed criterion for insoluble graphs. In particular, we prove that some of the conditions used to prove immateriality are necessary; when they are not satisfied, materiality is possible. We discuss possible avenues and obstacles to proving necessity of the remaining conditions

Contests with Linear Externality in Prizes

This study examines contests in which prizes are affected linearly by aggregate effort. In particular, this research analyzes a contest among individuals as a benchmark to scrutinize the effects of prize externality and sharing-rule information on rent-dissipation rate and social welfare. Thereafter, the current study investigates two types of group contest with linear prize externality: one with private information on intra-group sharing rules and the other with public information on intra-group sharing rules. Results indicate as follows. (1) An increase in prize externality increases rent-dissipation rate but has no effect on social welfare. (2) The group contest with private information on sharing rules yields higher social welfare and lower rent-dissipation rate than the one with public information on sharing rules

Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

Contests with Externalities

This paper examines contests in which aggregate efforts generate positive externalities to participants . In such contests the equilibrium effort may exceed or fall short of the socially optimal level of effort. This paper derives the relationship between the equilibrium effort, the size of prize, and the socially optimal level of effort. The equilibrium effort proves to exceed the social optimum when it is less than the prize times the exponent R of the Tullock (1980) contest-success function. On the other hand, when the equilibrium effort is greater than the prize times the exponent R, it indeed falls short of the social optimum