Governments and enterprises strongly rely on incentives to generate favorable outcomes from social and strategic interactions between individuals, for example climate or environmental friendly actions. The incentives are usually modeled by payoffs in strategical games, such as the prisoner’s dilemma or the harmony game. Adjusting the incentives by changing the payoff parameters e.g. through tax schemes can favor cooperation (harmony) over defection (prisoner’s dilemma). Here, we show that if individuals engage in strategic interactions in multiple domains, incentives can fail and the final outcome, cooperation or defection, is dominated by the initial state of the system. Our findings highlight the importance to take the multilayer structure of human interactions into account and emphasize the importance to rethink payoff-based incentives.
Failure of incentives in multiplex networks
Kaj-Kolja Kleineberg, Dirk Helbing
The total knowledge contained within a collective supersedes the knowledge of even its most intelligent member. Yet the collective knowledge will remain inaccessible to us unless we are able to find efficient knowledge aggregation methods that produce reliable decisions based on the behavior or opinions of the collective’s members. It is often stated that simple averaging of a pool of opinions is a good and in many cases the optimal way to extract knowledge from a crowd. The method of averaging has been applied to analysis of decision-making in very different fields, such as forecasting, collective animal behavior, individual psychology, and machine learning. Two mathematical theorems, Condorcet’s theorem and Jensen’s inequality, provide a general theoretical justification for the averaging procedure. Yet the necessary conditions which guarantee the applicability of these theorems are often not met in practice. Under such circumstances, averaging can lead to suboptimal and sometimes very poor performance. Practitioners in many different fields have independently developed procedures to counteract the failures of averaging. We review such knowledge aggregation procedures and interpret the methods in the light of a statistical decision theory framework to explain when their application is justified. Our analysis indicates that in the ideal case, there should be a matching between the aggregation procedure and the nature of the knowledge distribution, correlations, and associated error costs. This leads us to explore how machine learning techniques can be used to extract near-optimal decision rules in a data-driven manner. We end with a discussion of open frontiers in the domain of knowledge aggregation and collective intelligence in general.
Rescuing Collective Wisdom when the Average Group Opinion Is Wrong
Andres Laan, Gabriel Madirolas, and Gonzalo G. de Polavieja
Front. Robot. AI, 06 November 2017 | https://doi.org/10.3389/frobt.2017.00056
Swarm intelligence is the discipline that deals with the study of self-organizing processes both in nature and in artificial systems. Researchers in ethology and animal behavior have proposed a number of models to explain interesting aspects of collective behaviors such as movement coordination, shape-formation or decision making. Recently, algorithms and methods inspired by these models have been proposed to solve difficult problems in many domains. ANTS 2018 will give researchers in swarm intelligence the opportunity to meet, to present their latest research, and to discuss current developments and applications.
Eleventh International Conference on Swarm Intelligence
October 29-31, 2018. Rome, Italy
Falling oil revenues and rapid urbanization are putting a strain on the budgets of oil producing nations which often subsidize domestic fuel consumption. A direct way to decrease the impact of subsidies is to reduce fuel consumption by reducing congestion and car trips. While fuel consumption models have started to incorporate data sources from ubiquitous sensing devices, the opportunity is to develop comprehensive models at urban scale leveraging sources such as Global Positioning System (GPS) data and Call Detail Records. We combine these big data sets in a novel method to model fuel consumption within a city and estimate how it may change due to different scenarios. To do so we calibrate a fuel consumption model for use on any car fleet fuel economy distribution and apply it in Riyadh, Saudi Arabia. The model proposed, based on speed profiles, is then used to test the effects on fuel consumption of reducing flow, both randomly and by targeting the most fuel inefficient trips in the city. The estimates considerably improve baseline methods based on average speeds, showing the benefits of the information added by the GPS data fusion. The presented method can be adapted to also measure emissions. The results constitute a clear application of data analysis tools to help decision makers compare policies aimed at achieving economic and environmental goals.
Big Data Fusion to Estimate Fuel Consumption: A Case Study of Riyadh
Adham Kalila, Zeyad Awwad, Riccardo Di Clemente, Marta C. González
Historically, network science focused on static networks, in which nodes are connected by permanent links. However, in networked systems ranging from protein-protein interactions to social networks, links change. Although it might seem that permanent links would make it easier to control a system, Li et al. demonstrate that temporality has advantages in real and simulated networks. Temporal networks can be controlled more efficiently and require less energy than their static counterparts.
The fundamental advantages of temporal networks
A. Li, S. P. Cornelius, Y.-Y. Liu, L. Wang, A.-L. Barabási
Science 24 Nov 2017:
Vol. 358, Issue 6366, pp. 1042-1046