Filippo Zimmaro

Successful collective action on issues from climate change to the maintenance of democracy depends on societal properties such as cultural tightness and social cohesion. How these properties evolve is not well understood because they emerge from a complex interplay between beliefs and behaviors that are usually modeled separately. Here, we address this challenge by developing a game-theoretic framework incorporating norm-utility models to study the coevolutionary dynamics of cooperative behavior, expressed belief, and norm-utility preferences. We show that the introduction of evolving beliefs and preferences into the Snowdrift game and Prisoner’s Dilemma leads to a proliferation of evolutionary stable equilibria, each with different societal properties. In particular, we find that reduced material benefits from cooperation can be associated with an expected increase in cultural tightness (the degree to which norms are strong and shared, so that individuals behave in accordance with widely held beliefs) and an expected reduction in social homogeneity and cohesion (the extent to which individuals belong to a single well-defined group with similar beliefs, behaviors, and preferences). Loss of social homogeneity occurs via a process of evolutionary branching, in which a population fragments into two distinct social groups with strikingly different characteristics. The groups that emerge differ not only in their willingness to cooperate, but also in their expressed beliefs about cooperation and in their preferences for conformity and coherence of their behaviors and expressed beliefs. These results have implications for our understanding of the resilience of cooperation and collective action in times of crisis.

We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise in scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today’s AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: these countries account for a large share of global GDP and energy use and are therefore expected to be most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion. Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth.

We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise of scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today's AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: those countries are the ones that contribute most to global GDP production and energy consumption, and are expected to be the most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion. Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth.

We study the concept of _Economic Productivity of Energy_ (EPE), defined as the ratio of GDP produced and energy consumed at country or groups of countries level. We examine its behaviour over time using both historical data and recent, detailed databases. Our study compares the EPE for three different groups: underdeveloped, developing, and advanced countries. The underdeveloped countries exhibit the highest EPE, while an inversion occurs between developing and advanced countries around 2004, driven by a steady growth in EPE of the advanced economies from 1980 to 2018. Notably, during the onset of the first industrial revolution in England and Wales, the EPE decreased dramatically, with the trend reversing only decades later. We argue that, given AI’s current status as an energy-intensive technology, the risk of a collapse in EPE is significant, in analogy with the first industrial revolution. However, AI is projected to generate GDP growth several times greater than the one seen in the first postwar (WW2) decade in Western societies. Additionally, AI’s impact and adoption are expected to be more pronounced in advanced economies. We stress the need to investigate further these crucial questions: whether, when, and how the AI revolution will affect the currently positive global trend in EPE.

Two of the main factors shaping an individual’s opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases’ strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network.