118 research outputs found
Replication, Communication, and the Population Dynamics of Scientific Discovery
Many published research results are false, and controversy continues over the
roles of replication and publication policy in improving the reliability of
research. Addressing these problems is frustrated by the lack of a formal
framework that jointly represents hypothesis formation, replication,
publication bias, and variation in research quality. We develop a mathematical
model of scientific discovery that combines all of these elements. This model
provides both a dynamic model of research as well as a formal framework for
reasoning about the normative structure of science. We show that replication
may serve as a ratchet that gradually separates true hypotheses from false, but
the same factors that make initial findings unreliable also make replications
unreliable. The most important factors in improving the reliability of research
are the rate of false positives and the base rate of true hypotheses, and we
offer suggestions for addressing each. Our results also bring clarity to verbal
debates about the communication of research. Surprisingly, publication bias is
not always an obstacle, but instead may have positive impacts---suppression of
negative novel findings is often beneficial. We also find that communication of
negative replications may aid true discovery even when attempts to replicate
have diminished power. The model speaks constructively to ongoing debates about
the design and conduct of science, focusing analysis and discussion on precise,
internally consistent models, as well as highlighting the importance of
population dynamics
Social Conformity Despite Individual Preferences for Distinctiveness
We demonstrate that individual behaviors directed at the attainment of
distinctiveness can in fact produce complete social conformity. We thus offer
an unexpected generative mechanism for this central social phenomenon.
Specifically, we establish that agents who have fixed needs to be distinct and
adapt their positions to achieve distinctiveness goals, can nevertheless
self-organize to a limiting state of absolute conformity. This seemingly
paradoxical result is deduced formally from a small number of natural
assumptions, and is then explored at length computationally. Interesting
departures from this conformity equilibrium are also possible, including
divergence in positions. The effect of extremist minorities on these dynamics
is discussed. A simple extension is then introduced, which allows the model to
generate and maintain social diversity, including multimodal distinctiveness
distributions. The paper contributes formal definitions, analytical deductions,
and counterintuitive findings to the literature on individual distinctiveness
and social conformity.Comment: 11 pages, 6 figures, appendi
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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 Origins of Options
Most research on decision making has focused on how human or animal decision makers choose between two or more options, posed in advance by the researchers. The mechanisms by which options are generated for most decisions, however, are not well understood. Models of sequential search have examined the trade-off between continued exploration and choosing one’s current best option, but still cannot explain the processes by which new options are generated. We argue that understanding the origins of options is a crucial but untapped area for decision making research. We explore a number of factors which influence the generation of options, which fall broadly into two categories: psycho-biological and socio-cultural. The former category includes factors such as perceptual biases and associative memory networks. The latter category relies on the incredible human capacity for culture and social learning, which doubtless shape not only our choices but the options available for choice. Our intention is to start a discussion that brings us closer toward understanding the origins of options
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