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Information science --- Technology --- Social aspects

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E-books --- Debts, Public --- Budget deficits --- Global Financial Crisis, 2008-2009 --- Government policy --- Global Financial Crisis (2008-2009) --- Ireland --- Economic policy. --- Global Financial Crisis, 2008-2009. --- Global Economic Crisis, 2008-2009 --- Subprime Mortgage Crisis, 2008-2009 --- Financial crises --- Deficits, Budget --- Budget --- Deficit financing --- Debts, Government --- Government debts --- National debts --- Public debt --- Public debts --- Sovereign debt --- Debt --- Bonds --- Government policy. --- 2008-2009

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Child --- Statistics --- Children --- Enfants --- Statistiques --- Childhood --- Kids (Children) --- Pedology (Child study) --- Youngsters --- Age groups --- Families --- Life cycle, Human --- Child. --- Statistics. --- Children - Statistics

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The growing peer effects literature pays particular attention to the role of stars. We decompose the causal effect of hiring a star in terms of the productivity impact on: 1) co-located incumbents and 2) new recruits. Using longitudinal university department-level data we report that hiring a star does not increase overall incumbent productivity, although this aggregate effect hides offsetting effects on related (positive) versus unrelated (negative) colleagues. However, the primary impact comes from an increase in the average quality of subsequent recruits. This is most pronounced at mid-ranked institutions, suggesting implications for the socially optimal spatial organization of talent.

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We model a key step in the innovation process, hypothesis generation, as the making of predictions over a vast combinatorial space. Traditionally, scientists and innovators use theory or intuition to guide their search. Increasingly, however, they use artificial intelligence (AI) instead. We model innovation as resulting from sequential search over a combinatorial design space, where the prioritization of costly tests is achieved using a predictive model. We represent the ranked output of the predictive model in the form of a hazard function. We then use discrete survival analysis to obtain the main innovation outcomes of interest - the probability of innovation, expected search duration, and expected profit. We describe conditions under which shifting from the traditional method of hypothesis generation, using theory or intuition, to instead using AI that generates higher fidelity predictions, results in a higher likelihood of successful innovation, shorter search durations, and higher expected profits. We then explore the complementarity between hypothesis generation and hypothesis testing; potential gains from AI may not be realized without significant investment in testing capacity. We discuss the policy implications.