In the first part of the paper, the field of agent-based modeling (ABM) is discussed focusing on the role of generative theories, aiming at explaining phenomena by growing them. After a brief analysis of the major strengths of the field some crucial weaknesses are analyzed. In particular, the generative power of ABM is found to have been underexploited, as the pressure for simple recipes has prevailed and shadowed the application of rich cognitive models. In the second part of the paper, the renewal of interest for Computational Social Science (CSS) is focused upon, and several of its variants, such as deductive, generative, and complex CSS, are identified and described. In the concluding remarks, an interdisciplinary variant, which takes after ABM, reconciling it with the quantitative one, is proposed as a fundamental requirement for a new program of the CSS.

Social norms can help solve pressing societal challenges, from mitigating climate change to reducing the spread of infectious diseases. Despite their relevance, how norms shape cooperation among strangers remains insufficiently understood. Influential theories also suggest that the level of threat faced by different societies plays a key role in the strength of the norms that cultures evolve. Still little causal evidence has been collected. Here we deal with this dual challenge using a 30-day collective-risk social dilemma experiment to measure norm change in a controlled setting. We ask whether a looming risk of collective loss increases the strength of cooperative social norms that may avert it. We find that social norms predict cooperation, causally affect behavior, and that higher risk leads to stronger social norms that are more resistant to erosion when the risk changes. Taken together, our results demonstrate the causal effect of social norms in promoting cooperation and their role in making behavior resilient in the face of exogenous change.

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