META-REP: A Meta-scientific Programme to Analyse and Optimise Replicability in the Behavioural, Social, and Cognitive Sciences (SPP 2317)

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How an academic system can achieve a trustworthy knowledge base: Analyzing reform proposals in an agent-based modeling approach

In several scientific fields, evidence has accumulated that the scientific literature is much less robust and trustworthy than desired. The prevalence of unreliable findings poses a problem for scientific progress and for the application of research output, constituting a “replication crisis”. From a top-down perspective, this leads to an academic governance challenge: How can an academic system be structured to perform better? Furthermore, as many aspects of research activity are not top-down regulated, a complementary challenge concerns bottom-up or self-organizing processes: Which practices are worth adopting when thousands of autonomous researchers interact and follow their own goals?

Many reform suggestions are hard to be tested empirically, as (quasi-)experimental interventions are difficult, impossible, or must be performed on too small of a scale for definitive conclusions. In addition to empirical tests, theoretical tests are necessary as well. Verbal descriptions are usually insufficient to describe the behavior of complex systems, such as academia. A complementary approach is to simulate the consequences of structural reforms in agent-based models (ABM).

ABMs can be useful, even when they omit important features, because they focus research on clear, algorithmic proposals instead of vague, wishful ones. This project aims to extend existing models of science by evaluating recent proposals of the reform movement, and situating the results in three different disciplines targeted in the priority program. Specifically the selected proposals (a) have momentum in the community, (b) have a dynamic social aspect (in contrast to static statistical considerations), (c) can fruitfully be tackled with ABMs, and (d) have not been sufficiently addressed with social models yet.

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