Fabian Dvorak, Urs Fischbacher, Katrin Schmelz

The Economic Journal, ueae085,

We provide systematic insights on strategic conformist—as well as anti-conformist—behaviour in situations where people are evaluated, i.e., where an individual has to be selected for reward (e.g., promotion) or punishment (e.g., layoffs). To affect the probability of being selected, people may attempt to fit in or stand out in order to affect the chances of being noticed or liked by the evaluator. We investigate such strategic incentives for conformity or anti-conformity experimentally in three different domains: facts, taste and creativity. To distinguish conformity and anti-conformity from independence, we introduce a new experimental design that allows us to predict participants’ independent choices based on transitivity. We find that the prospect of punishment increases conformity, while the prospect of reward reduces it. Anti-conformity emerges in the prospect of reward, but only under specific circumstances. Similarity-based selection (i.e., homophily) is much more important for the evaluators’ decisions than salience. We also employ a theoretical approach to illustrate strategic key mechanisms of our experimental setting.

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Alexis Pietak, Michael Levin

Regulatory networks such as gene regulatory networks (GRNs) are critically important for efforts in biomedicine and synthetic biology. They have classically been viewed as mechanistic, “clockwork-like” systems, assumed to require direct changes to network topology via genetic modification to effect significant, stable changes in their output functions. This perspective limits therapeutic approaches, suggesting a need for alternative conceptual framing. Here we show how regulatory networks can behave as analog computational agents to perform sophisticated information processing, driven by patterns of stimulus inputs, without a change in network topology. We introduce and develop a new conceptual and computational framework for working with regulatory networks called the Regulatory Network Machine (RNM). Given a regulatory network model, our RNM framework enables the construction of detailed maps that embody the “software-like” nature of a regulatory network, providing easy identification of the specific interventions necessary to achieve desired outcomes. We demonstrate the use of our RNM framework to gain insights into important biological examples including yeast osmoadaptation, PI3K/AKT/mTor cross-signaling cascades, and embryonic stem cell differentiation. Importantly, we show how system-level outcomes can be induced in a biological system without requiring genetic rewiring. Our RNM approach also elucidates system factors that support the innate computational capabilities of regulatory networks, and ascertains the interventions that provide the most control for the least amount of effort. Ultimately, we hope to use insights gained from our RNM framework to expand the horizons of biomedicine, providing an effective avenue to move beyond “single-factor, single treatment” and “one-constant-dose” biomedical paradigms.

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Abigail Devereaux, Roger Koppl

Economists model knowledge use and acquisition as a cause-and-effect calculus associating observations made by a decision-maker about their world with possible underlying causes. Knowledge models are well-established for static contexts, but not for contexts of innovative and unbounded change. We develop a representation of knowledge use and acquisition in open-ended evolutionary systems and demonstrate its primary results, including that observers embedded in open-ended evolutionary systems can agree to disagree and that their ability to theorize about their systems is fundamentally local and constrained to their frame of reference what we call frame relativity. The results of our framework formalize local knowledge use, the many-selves interpretation of reasoning through time, and motivate the emergence of nonlogical modes of reasoning like institutional and aesthetic codes.

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Nicolò Gozzi, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Marco Ajelli, Alessandro Vespignani, Nicola Perra

Epidemics Volume 49, December 2024, 100805

The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models’ preliminary assessment about Omicron’s transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises.

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