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In this paper, we investigate how the choice to conduct interdisciplinary work affects a researcher's career. Using data on 23,926 articles published by 6,105 researchers affiliated with the University of Florida in the period 2008-2013, we show that synthesizing knowledge from diverse fields pays off in terms of reputation. However, if combining too-distant research fields, the impact of a work is penalized. Moreover, research conducted balancing the contribution of different scientific fields has a negative impact on the reputation of scientists in terms of the number of citations but a positive impact on the diffusion of knowledge across other disciplines. Our findings are robust to a number of controls, including individual, time, and field of study fixed effects, and they apply to all investigators regardless of their gender, collaboration behavior, performance, and affiliation. All in all, despite its public benefits, interdisciplinary research comes with a cost for a researcher's academic career. This trade-off poses challenging questions to policymakers.

There is considerable discussion on the so-called “mainstream pluralism”, which stems from the growth and coexistence of new research programs in economics that significantly deviate from the neoclassical core. Other disciplines have actively contributed to the birth of such programs, that are carried on by different, often separated communities of researchers. Although “mainstream pluralism” is not the pluralism heterodox economists and students groups have sought for in the recent decades, its persistence over time might provide a possible precondition for the advent of pluralism in economics. While the literature tends to regard mainstream pluralism as a transitory state towards a new, post-neoclassical, mainstream, this paper contributes to the debate by bringing in a different perspective, focusing on economics’ fragmentation and the necessity of specialization. We adopt a “late Kuhnian” framework (derived from Kuhn’s late works on specialization), considering not scientific revolutions but specialization as key engine of progress in science, and interpret mainstream pluralism as the result of economics’ recent growth in size and dive rsity. To account for the necessity of specialization in economics, we employ Ronald Heiner’s work on the competence-difficulty gap, as well as the evidence offered in some recent studies about the impact of the “burden” of previously accumulated knowledge on innovative behaviour. After a bird’s eye view on the recent history of economics in relation to other disciplines (and an analysis of Herbert Gintis’s “unity of behavioral sciences” proposal as possible new mains tream), we discuss the possibility that today’s “mainstream pluralism” might persist over time.

Agents capable of adaptive behavior can be obtained by means of AI tools. Thanks to these, they develop the ability to vary their Behavior in order to achieve satisfying results in the simulated environment. In the paper, artificially intelligent agents play an iterated prisoner' s dilemma against agents that reproduce (in a fix way) strategies that have emerged in Axelrod' s toumament. The objective of the adaptive agent is to earn a payoff higher than one of the Tit-for-tat, the strategy which has shown the better performance in the Axelrod's experimental setup. In the work, Genetic Algorithms are employed to produce and modify rules that are apt to achieve the set task. The adaptive dynamics is analysed in depth in order to understand the issues related to the codification of knowledge and to the evaluation of diverse strategies. In order to highlight different nuances of these matters we have amended the method as to improve it and experimented different knowledge's codifications.

This paper presents the analysis of a dataset of publications in economics that makes use of simulations. Data areas explored in order to obtain information about diffusion of simulation techniques in time and across sub-disciplines. Moreover, following Robert Axelrod's concerns about the difficulties in sharing simulation models and their outputs, some peculiarities in the communication process among 'simulators' are highlighted.

The paper proposes an experimental setup to compare different representations of artificial adaptive agents (genetic algorithms, artificial neural networks, and classifier systems) and suggests some criteria to assess equivalence and robustness of performance. In economie theory, the use of artificial adaptive agents as substitutes for the homo oeconomicus raises important methodological issues. While the reductionist approach grounded on Olympic rationality offers full rationality as the unique reference point for problem solving, weaker notions of rationality generate a variety of processes and outcomes of decision-making. The paper gives some suggestions on sensitivity of the behaviour of agents to the algorithmic choice and to the codification of knowledge. Preliminary results show that in an iterated prisoner's dilemma interesting patterns of behaviour (such as strategies that perform better than the tit-for-tat) emerge.