12,736 research outputs found
Democracy under uncertainty: The ‘wisdom of crowds’ and the free-rider problem in group decision making
We introduce a game theory model of individual decisions to cooperate by contributing personal resources to group decisions versus by free-riding on the contributions of other members. In contrast to most public-goods games that assume group returns are linear in individual contributions, the present model assumes decreasing marginal group production as a function of aggregate individual contributions. This diminishing marginal returns assumption is more realistic and generates starkly different predictions compared to the linear model. One important implication is that, under most conditions, there exist equilibria where some, but not all members of a group contribute, even with completely self-interested motives. An agent-based simulation confirms the individual and group advantages of the equilibria in which behavioral asymmetry emerges from a game structure that is a priori perfectly symmetric for all agents (all agents have the same payoff function and action space, but take different actions in equilibria). And a behavioral experiment demonstrates that cooperators and free-riders coexist in a stable manner in groups performing with the non-linear production function. A collateral result demonstrates that, compared to a ―dictatorial‖ decision scheme guided by the best member in a group, the majority-plurality decision rules can pool information effectively and produce greater individual net welfare at equilibrium, even if free-riding is not sanctioned. This is an original proof that cooperation in ad hoc decision-making groups can be understood in terms of self-interested motivations and that, despite the free-rider problem, majority-plurality decision rules can function robustly as simple, efficient social decision heuristics.group decision making under uncertainty, free-rider problem, majority-plurality rules, marginally-diminishing group returns, evolutionary games, behavioral experiment
Statistical inference in bipartite networks applied to social dilemmas and human microbial systems
La predicció de ‘links’ o enllaços en xarxes complexes és un problema de molta importà ncia, degut a la utilitat prà ctica que implica. La capacitat de predir enllaços correctament en una xarxa és, però, també una conseqüència directa de la comprensió del funcionament i de les dinà miques del sistema que s’estudia. En aquesta tesi doctoral, s’explora el problema de la predicció interpretable d’enllaços en xarxes complexes. En particular, l’anà lisi es centra en xarxes bipartides amb diferents tipus d’enllaços, degut a la seva presència en multitud de sistemes socials i naturals, aixà com a la seva capacitat d’analitzar diferents tipus d’interaccions.
En aquest sentit, es presenta una famÃlia de models amb els quals és possible fer prediccions interpretables d’enllaços en aquest tipus de xarxes. Posteriorment, aquests models s’apliquen a dos problemes de diferents disciplines. En primer lloc, considerem un experiment social en el que un grup nombrós de persones pren decisions estratègiques en el context de la teoria de jocs. Observem que és possible agrupar les persones segons el seu comportament coÅ€lectiu a l’hora de prendre decisions. En funció d’aquests grups, podem predir correctament aproximadament el 75% de les decisions. En segon lloc, s’estudia un problema de microbiota intestinal humana en el que tenim mostres microbials d’un nombre elevat de pacients. De manera anà loga al problema anterior, intentem trobar grups de pacients basant-nos en les similituds del seus perfils microbials. D’acord amb aquests grups, aconseguim predir al voltant d’un 80% de les abundà ncies.
En conclusió, es demostra que és possible aplicar aquesta famÃlia de mètodes a problemes molt diferents, de tal manera que podem construir models predictius i interpretables, basats en la capacitat d’identificar grups o comunitats de nodes, aixà com de monitoritzar les interaccions entre aquestes comunitats.La predicción de ‘links’ o enlaces en redes complejas es un problema de suma importancia debido a la utilidad práctica que comporta. Sin embargo, la capacidad de predecir links correctamente en una red, es también la consecuencia de la comprensión del funcionamiento y las dinámicas del sistema que se estudia. En esta tesis, exploramos el problema de la predicción interpretable de links en redes complejas. En particular, nos centramos en redes bipartidas con varios tipos de links, debido a su ubicuidad en multitud de sistemas sociales y naturales, asà como a la riqueza formal que aportan a nivel de las interacciones.
A tal efecto, presentamos una familia de modelos con los que es posible hacer predicciones de links interpretables en dichas redes y la aplicamos a dos problemas de diferentes campos. En primer lugar, consideramos un experimento social en el que un grupo numeroso de personas toma decisiones estratégicas en el contexto de la teorÃa de juegos. Observamos que podemos agrupar a las personas por su comportamiento colectivo a la hora de tomar decisiones y que, en base a esos grupos, podemos predecir correctamente el 75% de las decisiones aproximadamente.
En segundo lugar, estudiamos un problema de microbiota intestinal humana en el que tenemos muestras microbiales de un número elevado de pacientes. De manera análoga al problema anterior, intentamos encontrar grupos de pacientes por las similitudes en sus perfiles microbiales y, sobre esa base, predecir las abundancias de las diferentes especies de microbios. Conseguimos predecir aproximadamente un 80% de las abundancias.
En definitiva, demostramos que es posible aplicar nuestros métodos a problemas muy diferentes, de tal manera que podemos construir modelos predictivos e interpretables, basados en la capacidad de identificar grupos o comunidades de nodos, asà como de monitorizar las interacciones entre dichas comunidades.Link prediction in complex networks is a very important problem due to its practical importance. However, the ability of predicting links successfully arises naturally from a good understanding of the functioning and the dynamics of the system under study. In this thesis, we explore the problem of interpretable link prediction in complex networks. In particular, we focus on multilink bipartite networks; first, because bipartite networks are ubiquitous in many natural and social systems and second, because the existence of multiple links allows us to analyze different types of interactions.
To that end, we present a family of models that can make interpretable link prediction in this kind of networks and we apply them to two different problems. In the first problem, we consider a social experiment in which a large group of people make strategic decisions in a game theoretical context. We observe that it is possible to find groups of people according to their collective strategic behaviors (i.e., how do they make decisions) and that it is possible to make link prediction upon those groups. In our case we can successfully predict around 75% of the decisions.
The second problem is a human microbiology one. We have data on gut microbiome samples from a large number of patients. In a similar fashion, we look for groups of patients according to similarities in their microbial profiles. We then make predictions of microbial abundances using that group structure with an approximately 80% accuracy rate.
In conclusion, we show that it is possible to implement our methods to problems that are very different in their nature, so that we can build predictive and interpretable models that work on the ability to identify groups or communities of nodes and track the interactions among those communities
Ordering in spatial evolutionary games for pairwise collective strategy updates
Evolutionary games are studied with players located on a square
lattice. During the evolution the randomly chosen neighboring players try to
maximize their collective income by adopting a random strategy pair with a
probability dependent on the difference of their summed payoffs between the
final and initial state assuming quenched strategies in their neighborhood. In
the case of the anti-coordination game this system behaves alike an
anti-ferromagnetic kinetic Ising model. Within a wide region of social dilemmas
this dynamical rule supports the formation of similar spatial arrangement of
the cooperators and defectors ensuring the optimum total payoff if the
temptation to choose defection exceeds a threshold value dependent on the
sucker's payoff. The comparison of the results with those achieved for pairwise
imitation and myopic strategy updates has indicated the relevant advantage of
pairwise collective strategy update in the maintenance of cooperation.Comment: 9 pages, 6 figures; accepted for publication in Physical Review
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
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