Cognition & Economics & ICT  (CEI) Session 1

Abstract: By and large, an artwork has low intrinsic value; yet some can be sold at auctions for hundred of millions of dollars. The value of an artwork can in part be explained by the artist’s reputation, which can be measured by the prestige of institutions where she is exhibited over time. Yet, little is known about the relationship between an artist's exhibition history and her sales price. To shed light on this relationship, we use the largest dataset over collected about the art world. This unique dataset contains information about over 700,000 exhibitions of about 450,000 artists and covers the period 1980-2015. It also contains the 3 millions auctions sales prices which were generated by these artists during the same period. We find the presence of "rich clubs": artists who have exhibited at institutions of very high (low) prestige tend to keep exhibiting at institutions of very high (low) prestige; however, transitions between institutions of high and low prestige are quite rare. We also show that a higher average sales price is related to having exhibited more, in a larger variety of venues, and at more prestigious institutions. These findings enrich our understanding of the relationship between artists' performance and how they acquire reputation over time. Close

Abstract: While in cognitive sciences much research has been done about how humans learn in relatively simple problems, much less is known on the learning process associated with more complex environments. Here, we present the results of an experiment in which participants learn causal structures of varying complexity. We find that the rate of learning follows a power law decay in both the simple and complex cases where accuracy remains by a constant amount lower for the complex than for the simple case. The power law decay is very slow compared with the learning curve of fully rational computational agents. To overcome their bounded rationality, human participants resort to more or less explorative belief updates. The interplay of explorations into new solution subspaces and their integration with past experience for learning involves a subtle trial-and-error process, driven by timely explorative belief updates. The outcomes of these "wild" beliefs are then either rejected or partially integrated with past experience. The slow power law decay of human learning stems on the one hand from the heavy-tailed learning process, made of few wild belief updates combined with some integrative exploitation of past beliefs, and on the other hand, from the heavy tailed distribution of waiting times between belief updates, which is reminiscent of human priority queueing processes and how they map into a problem of economy of time as a non-storable scarce resource. We find that wild belief updates occur more frequently and waiting times are more heavy-tailed in the 4-node complex cases than in the 3-node simple cases. The increased difficulty forces more exploration and increases mental task loading to integrate outcomes of belief updates. Our findings contribute to a more general understanding of how humans learn about complex systems, and suggest that more complex problems require more exploration and additional learning time. Close

Abstract: We propose a data-driven approach to study the history of scientific thinking. We work with a complex database whose core is the mathematical genealogy data (http://genealogy.math.ndsu.nodak.edu): the largest academic genealogy present on the web. This dataset provides the mentor-student links for 200K scientists from the 13th century until nowadays. This information is integrated with other attributes extracted from Wikipedia and Scopus or retrieved through machine learning procedures. We study the database at different levels: at a global level to compare the historical rankings of countries and disciplines, at the level of the genealogical tree and its partition, to assess the role of the academic lineages in the evolution of science and, finally, at an aggregate dynamical network level, showing the knowledge flows between countries and disciplines. With the first global approach we are able to find out the most important tipping point in the scientific history and to cluster countries and discipline according to their prevalence profile. The second approach based on genealogy show the presence of more or less 20 major families covering the 80% of the database. These lineages have geographical preferences and a strong specialization in term of disciplines. We show how scientific revolutions develop inside these structures with sudden changes in the topic composition. The third approach finally allows to understand the historical role of countries in flowing knowledge. While the most frequent countries in the DB, like US and Germany, result to be central in term of the degree, countries like France, Russia and Poland are responsible to guarantee the network connectivity. We categorize countries according to their role in exporting, absorbing and conserving academics. We show that, in the last decade, the 6% of the countries are producing the 80% of the worldwide academics. Close

Abstract: While polarization has become a growing threat to the deliberative traditions of established liberal democracies, authoritarian regimes have become more vulnerable to instability due to self-organized activism. Both polarization and social activism dovetail with the changing ways political information is communicated online. Our preliminary work demonstrates that text-mining methods can be used to reveal policy agendas and issue frames in Congressional deliberations by revealing the semantic expressions of polarization and how it unfolds in time, both generally and under specific topics [1]. Here we expand this work by incorporating additional legislative data and by studying the long-term interplay between political discourse, in the confines of legislative norms, and the less regulated setting of social activism on Instagram and Twitter. The goal is to quantitatively answer such questions as how much impact the Ferguson and similar social movements have in the U.S. legislative in-chambers political discourse? and which precise terminology is being used to frame the issue by both U.S. parties? For that we build longitudinal knowledge networks of semantically relevant terms, derived from online social activism, in legislative discourse. We then analyze these networks using spectral methods, shortest-paths and distance closure analysis [2,3] to study how social media chatter penetrates congressional deliberations. Finally, we uncover party-specific “Dog-Whistle” terms and their evolution, which allows us to study the semantic root of polarization and how it unfolds in time. [1] Correia, Rion B., K. N. Chan, & L.M. Rocha [2015]. “Polarization in the US Congress.” The 8th Annual Conference of the Comparative Agendas Project (CAP). Lisbon, Portugal, June 23-24, 2015. [2] T. Simas and L.M. Rocha [2015]. “Distance Closures on Complex Networks”. Network Science, 3(2):227-268. [3] G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini [2015]. “Computational fact checking from knowledge networks.” PLoS One. 10(6): e0128193. Close

Abstract: Reputation is crucial for online social and economic interactions among otherwise anonymous individuals, but concerns about its reliability and the possibility of fraud are mounting. Usually, information about prospective counterparts is incomplete, often being limited to an average success rate. Uncertainty on reputation is further increased by fraud, which is increasingly becoming a cause of concern. To understand how unreliable reputation affects the evolution of cooperation in social interactions, we have designed a laboratory experiment based on the framework of the Prisoner's Dilemma game. In our treatment participants could spend money to have their observable cooperativeness, i.e. their reputation, increased, while in the baseline scenario participants' real reputations are unveiled. We find that the aggregate cooperation level is practically unchanged for both treatments, i.e., global behavior does not seem to be affected by unreliable reputations. However, at the individual level we find two distinct types of behavior, one of reliable subjects and one of cheaters, where the latter artificially fake their reputation in almost every interaction. Cheaters end up being better off than honest individuals, who not only keep their true reputation but are also more cooperative. In practice, the cost of the fraud is supported by honest players, while cheaters earn the same as in a truthful environment. As a result, inequality in the population, as measured by the Gini coefficient of the wealth distribution, increases, showing the largely harmful effects of reputation fraud. These findings point to the importance of ensuring the truthfulness of reputation for a more equitable and fair society. This experimental study has been recently accepted for publication on Scientific Reports. Close

Abstract: Policy makers suffer from an intrinsic difficulty when addressing challenges like climate change, financial crises, governance of pandemics, or energy sufficiency: the impact and unintended consequences of public action are increasingly hard to anticipate. Such challenges are global and connect policies across different sectors. When faced with such highly interconnected challenges, societies still tend to address subsystems and so fail to achieve systemic change. GSS can drive change by helping develop an integrated policy perspective on global challenges; and developing a research agenda that will tackle the fundamental research challenges. A case in point is the area of economic modeling after the financial crisis. New concepts and tools – for instance to analyze the network of actors in financial markets - will be developed in collaboration between researchers in GSS and policy bodies. Other policy areas include urban dynamics and climate change where a combination of data from various sources (smart grids, mobility data, sensor data, socio-economic data...) with dynamical modeling will pave the way to new policy suggestions. Global Systems Science combines policy problems at global and local scales, the science of complex systems, policy informatics in which scientific knowledge is embedded in usable computer tools, and citizen engagement. GSS provides a practical way for members of the complex systems community to engage with policy problems and make a tangible difference to our complex world. https://www.futurelearn.com/courses/global-systems-science Close