Stefano Balietti

From social data mining to forecasting socio-economic crises

Abstract.The purpose of this White Paper of the EU Support Action “Visioneer”(see www.visioneer.ethz.ch) is to address the following goals: 1.Develop strategies to quickly increase the objective knowledge about social and economic systems.2.Describe requirements for efficient large-scale scientific data mining of anonymized social and economic data.3.Formulate strategies how to collect stylized facts extracted from large data set.4.Sketch ways how to successfully build up centers for computational social science.5.Propose plans how to create centers for risk analysis and crisis forecasting.6.Elaborate ethical standards regarding the storage, processing, evaluation, and publication of social and economic data.

How to create an innovation accelerator

Abstract.The purpose of this White Paper of the EU Support Action “Visioneer” (see www.visioneer.ethz.ch) is to address the following goals: 1.Identify new ways of publishing, evaluating, and reporting scientific progress.2.Promote ICT solutions to increase the awareness of new emerging trends.3.Invent tools to enhance Europe’s innovation potential.4.Develop new strategies to support a sustainable technological development.5.Lay the foundations for new ways to reach societal benefits and respond to industrial needs using ICT.

From social simulation to integrative system design

Abstract.The purpose of this White Paper of the EU Support Action “Visioneer” (see www.visioneer.ethz.ch) is to address the following goals: 1.Develop strategies to build up social simulation capacities.2.Suggest ways to build up an “artificial societies” community that aims at simulating real and alternative societies by means of supercomputers, grid or cloud computing.3.Derive proposals to establish centers for integrative systems design.

Peer review and competition in the Art Exhibition Game

Significance Competition is an essential mechanism in increasing the effort and performance of human groups in real life. However, competition has side effects: it can be detrimental to creativity and reduce cooperation. We conducted an experiment called the Art Exhibition Game to investigate the effect of competitive incentives in environments where the quality of creative products and the amount of innovation allowed are decided through peer review. Our approach is general and can provide insights in domains such as clinical evaluations, scientific admissibility, and science funding. Our results show that competition leads to more innovation but also to more unfair reviews and to a lower level of agreement between reviewers. Moreover, competition does not improve the average quality of published works. To investigate the effect of competitive incentives under peer review, we designed a novel experimental setup called the Art Exhibition Game. We present experimental evidence of how competition introduces both positive and negative effects when creative artifacts are evaluated and selected by peer review. Competition proved to be a double-edged sword: on the one hand, it fosters innovation and product diversity, but on the other hand, it also leads to more unfair reviews and to a lower level of agreement between reviewers. Moreover, an external validation of the quality of peer reviews during the laboratory experiment, based on 23,627 online evaluations on Amazon Mechanical Turk, shows that competition does not significantly increase the level of creativity. Furthermore, the higher rejection rate under competitive conditions does not improve the average quality of published contributions, because more high-quality work is also rejected. Overall, our results could explain why many ground-breaking studies in science end up in lower-tier journals. Differences and similarities between the Art Exhibition Game and scholarly peer review are discussed and the implications for the design of new incentive systems for scientists are explained.

Fundamental and Real-World Challenges in Economics

In the same way as the Hilbert Program was a response to the foundational crisis of mathematics1, this article tries to formulate a research program for the socio-economic sciences. The aim of this contribution is to stimulate research in order to close serious knowledge gaps in mainstream economics that the recent financial and economic crisis has revealed. By identifying weak points of conventional approaches in economics, we identify the scientific problems which need to be addressed. We expect that solving these questions will bring scientists in a position to give better decision support and policy advice. We also indicate, what kinds of insights can be contributed by scientists from other research fields such as physics, biology, computer and social science. In order to make a quick progress and gain a systemic understanding of the whole interconnected socioeconomic-environmental system, using the data, information and computer systems available today and in the near future, we suggest a multi-disciplinary collaboration as most promising research approach.

On disciplinary fragmentation and scientific progress.

Why are some scientific disciplines, such as sociology and psychology, more fragmented into conflicting schools of thought than other fields, such as physics and biology? Furthermore, why does high fragmentation tend to coincide with limited scientific progress? We analyzed a formal model where scientists seek to identify the correct answer to a research question. Each scientist is influenced by three forces: (i) signals received from the correct answer to the question; (ii) peer influence; and (iii) noise. We observed the emergence of different macroscopic patterns of collective exploration, and studied how the three forces affect the degree to which disciplines fall apart into divergent fragments, or so-called “schools of thought”. We conducted two simulation experiments where we tested (A) whether the three forces foster or hamper progress, and (B) whether disciplinary fragmentation causally affects scientific progress and vice versa. We found that fragmentation critically limits scientific progress. Strikingly, there is no effect in the opposite causal direction. What is more, our results shows that at the heart of the mechanisms driving scientific progress we find (i) social interactions, and (ii) peer disagreement. In fact, fragmentation is increased and progress limited if the simulated scientists are open to influence only by peers with very similar views, or when within-school diversity is lost. Finally, disciplines where the scientists received strong signals from the correct answer were less fragmented and experienced faster progress. We discuss model’s implications for the design of social institutions fostering interdisciplinarity and participation in science.

nodeGame: Real-time, synchronous, online experiments in the browser

AbstractnodeGame is a free, open-source JavaScript/ HTML5 framework for conducting synchronous experiments online and in the lab directly in the browser window. It is specifically designed to support behavioral research along three dimensions: (i) larger group sizes, (ii) real-time (but also discrete time) experiments, and (iii) batches of simultaneous experiments. nodeGame has a modular source code, and defines an API (application programming interface) through which experimenters can create new strategic environments and configure the platform. With zero-install, nodeGame can run on a great variety of devices, from desktop computers to laptops, smartphones, and tablets. The current version of the software is 3.0, and extensive documentation is available on the wiki pages at http://nodegame.org.

Meritocratic matching can dissolve the efficiency-equality tradeoff: the case of voluntary contributions

Real-world institutions dealing with social dilemma situations are based on mechanisms that are rarely implemented without flaw. Usually real-world mechanisms are noisy and imprecise, that is, which we call ‘fuzzy’. We therefore conducted a novel type of voluntary contributions experiment where we test a mechanism by varying its fuzziness. We focus on a range of fuzzy mechanisms we call ‘meritocratic matching’. These mechanisms generalize the mechanism of ‘contribution-based competitive grouping’, and their basic function is to group players based on their contribution choices — i.e. high contributors with high contributors, and low contributors with low contributors. Theory predicts the following efficiency-equality tradeoff as a function of the mechanism’s inherent fuzziness: high levels of fuzziness should lead to maximal inefficiency, but perfect equality; decreasing fuzziness is predicted to improve efficiency, but at the cost of growing inequality. The main finding of our experimental investigation is that, contrary to tradeoff predictions, less fuzziness increases both efficiency and equality. In fact, these unambiguous welfare gains are partially realized already at levels where the mechanism is too fuzzy for any high-efficiency outcome to even be a Nash equilibrium.

Big data, privacy, and trusted web: What needs to be done

This perspective paper discusses challenges and risks of the information age, and the implications for the information and communication technologies that need to be built and operated. It addresses ethical and policy issues related with Big Data and how procedures for privacy-preserving data analyses can be established. It further proposes a concept for a future, self-organising and trusted Web and discusses recommended legal regulations as well as the infrastructure and institutions needed.

Adding noise to the institution: an experimental welfare investigation of the contribution-based grouping mechanism

Real-world institutions dealing with social dilemma situations are based on mechanisms that are rarely implemented without flaw. Usually real-world mechanisms are noisy and imprecise, that is, which we call ‘fuzzy’. We therefore conducted a novel type of voluntary contributions experiment where we test a mechanism by varying its fuzziness. We focus on a range of fuzzy mechanisms we call ‘meritocratic matching’. These mechanisms generalize the mechanism of ‘contribution-based competitive grouping’, and their basic function is to group players based on their contribution choices—i.e. high contributors with high contributors, and low contributors with low contributors. Theory predicts the following efficiency-equality tradeoff as a function of the mechanism’s inherent fuzziness: high levels of fuzziness should lead to maximal inefficiency, but perfect equality; decreasing fuzziness is predicted to improve efficiency, but at the cost of growing inequality. The main finding of our experimental investigation is that, contrary to tradeoff predictions, less fuzziness increases both efficiency and equality. In fact, these unambiguous welfare gains are partially realized already at levels where the mechanism is too fuzzy for any high-efficiency outcome to even be a Nash equilibrium.