Open science and modified funding lotteries can impede the natural selection of bad science.

Assessing scientists using exploitable metrics can lead to the degradation of research methods even without any strategic behaviour on the part of individuals, via 'the natural selection of bad science.' Institutional incentives to maximize metrics like publication quantity and impact drive this dynamic. Removing these incentives is necessary, but institutional change is slow. However, recent developments suggest possible solutions with more rapid onsets. These include what we call open science improvements, which can reduce publication bias and improve the efficacy of peer review. In addition, there have been increasing calls for funders to move away from prestige- or innovation-based approaches in favour of lotteries. We investigated whether such changes are likely to improve the reproducibility of science even in the presence of persistent incentives for publication quantity through computational modelling. We found that modified lotteries, which allocate funding randomly among proposals that pass a threshold for methodological rigour, effectively reduce the rate of false discoveries, particularly when paired with open science improvements that increase the publication of negative results and improve the quality of peer review. In the absence of funding that targets rigour, open science improvements can still reduce false discoveries in the published literature but are less likely to improve the overall culture of research practices that underlie those publications

Adoption as a Social Marker: Innovation Diffusion with Outgroup Aversion

Social identities are among the key factors driving behavior in complex societies. Signals of social identity are known to influence individual behaviors in the adoption of innovations. Yet the population-level consequences of identity signaling on the diffusion of innovations are largely unknown. Here we use both analytical and agent-based modeling to consider the spread of a beneficial innovation in a structured population in which there exist two groups who are averse to being mistaken for each other. We investigate the dynamics of adoption and consider the role of structural factors such as demographic skew and communication scale on population-level outcomes. We find that outgroup aversion can lead to adoption being delayed or suppressed in one group, and that population-wide underadoption is common. Comparing the two models, we find that differential adoption can arise due to structural constraints on information flow even in the absence of intrinsic between-group differences in adoption rates. Further, we find that patterns of polarization in adoption at both local and global scales depend on the details of demographic organization and the scale of communication. This research has particular relevance to widely beneficial but identity-relevant products and behaviors, such as green technologies, where overall levels of adoption determine the positive benefits that accrue to society at large.Comment: 26 pages, 10 figure

The form of uncertainty affects selection for social learning

Social learning is a critical adaptation for dealing with different forms of variability. Uncertainty is a severe form of variability where the space of possible decisions or probabilities of associated outcomes are unknown. We identified four theoretically important sources of uncertainty: temporal environmental variability; payoff ambiguity; selection-set size; and effective lifespan. When these combine, it is nearly impossible to fully learn about the environment. We develop an evolutionary agent-based model to test how each form of uncertainty affects the evolution of social learning. Agents perform one of several behaviours, modelled as a multi-armed bandit, to acquire payoffs. All agents learn about behavioural payoffs individually through an adaptive behaviour-choice model that uses a softmax decision rule. Use of vertical and oblique payoff-biased social learning evolved to serve as a scaffold for adaptive individual learning – they are not opposite strategies. Different types of uncertainty had varying effects. Temporal environmental variability suppressed social learning, whereas larger selection-set size promoted social learning, even when the environment changed frequently. Payoff ambiguity and lifespan interacted with other uncertainty parameters. This study begins to explain how social learning can predominate despite highly variable real-world environments when effective individual learning helps individuals recover from learning outdated social information

G4 Resolvase 1 tightly binds and unwinds unimolecular G4-DNA

It has been previously shown that the DHX36 gene product, G4R1/RHAU, tightly binds tetramolecular G4-DNA with high affinity and resolves these structures into single strands. Here, we test the ability of G4R1/RHAU to bind and unwind unimolecular G4-DNA. Gel mobility shift assays were used to measure the binding affinity of G4R1/RHAU for unimolecular G4-DNA-formed sequences from the Zic1 gene and the c-Myc promoter. Extremely tight binding produced apparent Kd's of 6, 3 and 4 pM for two Zic1 G4-DNAs and a c-Myc G4-DNA, respectively. The low enzyme concentrations required for measuring these Kd's limit the precision of their determination to upper boundary estimates. Similar tight binding was not observed in control non-G4 forming DNA sequences or in single-stranded DNA having guanine-rich runs capable of forming tetramolecular G4-DNA. Using a peptide nucleic acid (PNA) trap assay, we show that G4R1/RHAU catalyzes unwinding of unimolecular Zic1 G4-DNA into an unstructured state capable of hybridizing to a complementary PNA. Binding was independent of adenosine triphosphate (ATP), but the PNA trap assay showed that unwinding of G4-DNA was ATP dependent. Competition studies indicated that unimolecular Zic1 and c-Myc G4-DNA structures inhibit G4R1/RHAU-catalyzed resolution of tetramolecular G4-DNA. This report provides evidence that G4R1/RHAU tightly binds and unwinds unimolecular G4-DNA structure

Yin Yang 1 contains G-quadruplex structures in its promoter and 5′-UTR and its expression is modulated by G4 resolvase 1

Yin Yang 1 (YY1) is a multifunctional protein with regulatory potential in tumorigenesis. Ample studies demonstrated the activities of YY1 in regulating gene expression and mediating differential protein modifications. However, the mechanisms underlying YY1 gene expression are relatively understudied. G-quadruplexes (G4s) are four-stranded structures or motifs formed by guanine-rich DNA or RNA domains. The presence of G4 structures in a gene promoter or the 5′-UTR of its mRNA can markedly affect its expression. In this report, we provide strong evidence showing the presence of G4 structures in the promoter and the 5′-UTR of YY1. In reporter assays, mutations in these G4 structure forming sequences increased the expression of Gaussia luciferase (Gluc) downstream of either YY1 promoter or 5′-UTR. We also discovered that G4 Resolvase 1 (G4R1) enhanced the Gluc expression mediated by the YY1 promoter, but not the YY1 5′-UTR. Consistently, G4R1 binds the G4 motif of the YY1 promoter in vitro and ectopically expressed G4R1 increased endogenous YY1 levels. In addition, the analysis of a gene array data consisting of the breast cancer samples of 258 patients also indicates a significant, positive correlation between G4R1 and YY1 expressio

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Beyond collective intelligence: Collective adaptation

Data accessibility: This article has no additional data.Copyright © 2023 The Authors. We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.This research originated at a workshop at the Santa Fe Institute, funded by the National Science Foundation grant NSF BCS 1745154. A.M.B. was supported by the H. Mason Keeler Endowed Professorship in Sports Fisheries Management. C.G. was partially supported by the Defense Advanced Research Projects Agency, award no. FP00002636. M.G. was partially supported by NSF DRMS 1757211. M.G. and H.O. were partially supported by NSF SocPsych 1918490. E.L. was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. RS-2022-00165916)