839 research outputs found
Information sharing promotes prosocial behaviour
More often than not, bad decisions are bad regardless of where
and when they are made. Information sharing might thus be utilized to
mitigate them. Here we show that sharing information about strategy choice
between players residing on two different networks reinforces the evolution
of cooperation. In evolutionary games, the strategy reflects the action of each
individual that warrants the highest utility in a competitive setting. We therefore
assume that identical strategies on the two networks reinforce themselves by
lessening their propensity to change. Besides network reciprocity working in
favour of cooperation on each individual network, we observe the spontaneous
emergence of correlated behaviour between the two networks, which further
deters defection. If information is shared not just between individuals but also
between groups, the positive effect is even stronger, and this despite the fact
that information sharing is implemented without any assumptions with regard to
content
Quality Assurance for Human-Based Electronic Services: A Decision Matrix for Choosing the Right Approach
Analytical reasoning task reveals limits of social learning in networks
Social learning -by observing and copying others- is a highly successful
cultural mechanism for adaptation, outperforming individual information
acquisition and experience. Here, we investigate social learning in the context
of the uniquely human capacity for reflective, analytical reasoning. A hallmark
of the human mind is our ability to engage analytical reasoning, and suppress
false associative intuitions. Through a set of lab-based network experiments,
we find that social learning fails to propagate this cognitive strategy. When
people make false intuitive conclusions, and are exposed to the analytic output
of their peers, they recognize and adopt this correct output. But they fail to
engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit
an 'unreflective copying bias,' which limits their social learning to the
output, rather than the process, of their peers' reasoning -even when doing so
requires minimal effort and no technical skill. In contrast to much recent work
on observation-based social learning, which emphasizes the propagation of
successful behavior through copying, our findings identify a limit on the power
of social networks in situations that require analytical reasoning
The entropic basis of collective behaviour
We identify a unique viewpoint on the collective behaviour of intelligent agents. We first develop a highly general abstract model for the possible future lives these agents may encounter as a result of their decisions. In the context of these possibilities, we show that the causal entropic principle, whereby agents follow behavioural rules that maximize their entropy over all paths through the future, predicts many of the observed features of social interactions among both human and animal groups. Our results indicate that agents are often able to maximize their future path entropy by remaining cohesive as a group and that this cohesion leads to collectively intelligent outcomes that depend strongly on the distribution of the number of possible future paths. We derive social interaction rules that are consistent with maximum entropy group behaviour for both discrete and continuous decision spaces. Our analysis further predicts that social interactions are likely to be fundamentally based on Weber's law of response to proportional stimuli, supporting many studies that find a neurological basis for this stimulus-response mechanism and providing a novel basis for the common assumption of linearly additive 'social forces' in simulation studies of collective behaviour
Emergence of collective intonation in the musical performance of crowds
To be published in EPLTo be published in EP
Integrating evidence, politics and society: a methodology for the science–policy interface
There is currently intense debate over expertise, evidence and ‘post-truth’ politics, and how this is influencing policy formulation and implementation. In this article, we put forward a methodology for evidence-based policy making intended as a way of helping navigate this web of complexity. Starting from the premise of why it is so crucial that policies to meet major global challenges use scientific evidence, we discuss the socio-political difficulties and complexities that hinder this process. We discuss the necessity of embracing a broader view of what constitutes evidence—science and the evaluation of scientific evidence cannot be divorced from the political, cultural and social debate that inevitably and justifiably surrounds these major issues. As a pre-requisite for effective policy making, we propose a methodology that fully integrates scientific investigation with political debate and social discourse. We describe a rigorous process of mapping, analysis, visualisation and sharing of evidence, constructed from integrating science and social science data. This would then be followed by transparent evidence evaluation, combining independent assessment to test the validity and completeness of the evidence with deliberation to discover how the evidence is perceived, misunderstood or ignored. We outline the opportunities and the problems derived from the use of digital communications, including social media, in this methodology, and emphasise the power of creative and innovative evidence visualisation and sharing in shaping policy
Minimizing efforts in validating crowd answers
In recent years, crowdsourcing has become essential in a wide range of Web applications. One of the biggest challenges of crowdsourcing is the quality of crowd answers as workers have wide-ranging levels of expertise and the worker community may contain faulty workers. Although various techniques for quality control have been proposed, a post-processing phase in which crowd answers are validated is still required. Validation is typically conducted by experts, whose availability is limited and who incur high costs. Therefore, we develop a probabilistic model that helps to identify the most beneficial validation questions in terms of both, improvement of result correctness and detection of faulty workers. Our approach allows us to guide the experts work by collecting input on the most problematic cases, thereby achieving a set of high quality answers even if the expert does not validate the complete answer set. Our comprehensive evaluation using both real-world and synthetic datasets demonstrates that our techniques save up to 50% of expert efforts compared to baseline methods when striving for perfect result correctness. In absolute terms, for most cases, we achieve close to perfect correctness after expert input has been sought for only 20% of the questions
Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior
Despite the availability of very detailed data on financial market,
agent-based modeling is hindered by the lack of information about real trader
behavior. This makes it impossible to validate agent-based models, which are
thus reverse-engineering attempts. This work is a contribution to the building
of a set of stylized facts about the traders themselves. Using the client
database of Swissquote Bank SA, the largest on-line Swiss broker, we find
empirical relationships between turnover, account values and the number of
assets in which a trader is invested. A theory based on simple mean-variance
portfolio optimization that crucially includes variable transaction costs is
able to reproduce faithfully the observed behaviors. We finally argue that our
results bring into light the collective ability of a population to construct a
mean-variance portfolio that takes into account the structure of transaction
costsComment: 26 pages, 9 figures, Fig. 8 fixe
Curriculum and Teacher Education Reforms in Finland That Support the Development of Competences for the Twenty-First Century
Abstract This chapter analyzes how learning twenty-first century competences has been implemented in the Finnish educational context through the enactment of national and local level curricula and the design of a teacher education development program in a decentralized education system, in which teachers, schools, municipalities, and universities have high autonomy. The curricula and development program emphasize learning twenty-first century competences. Both were designed in collaboration with Finnish teachers and teacher educators, representatives from the Ministry of Education and Culture, the Association of Finnish Local and Regional Authorities, the Teacher’s Union, the Student’s Unions, and the Principal Association. The major actions taken to implement these changes included piloting, seminars and conferences, having different support and local level collaborations, and networking. According to recent evaluations, both endeavors – the development of national and local level curricula and a teacher education development program – have resulted in progress towards implementing twenty-first century competences in schools and for teacher education.Peer reviewe
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