Preface 1. Introduction: the problem of collective action 2. The prisoners' dilemma, chicken and other games in the provision of public goods 3. The two-person prisoners' dilemma supergame 4. The N-person prisoners' dilemma supergame 5. Altruism and superiority 6. The state 7. Epilogue: cooperation, the state and anarchy Annex Notes Bibliography Index.

The possibility of cooperation

Algorithmic systems that employ machine learning play an increasing role in making substantive decisions in modern society, ranging from online personalization to insurance and credit decisions to predictive policing. But their decision-making processes are often opaque -- it is difficult to explain why a certain decision was made. We develop a formal foundation to improve the transparency of such decision-making systems. Specifically, we introduce a family of Quantitative Input Influence (QII) measures that capture the degree of influence of inputs on outputs of systems. These measures provide a foundation for the design of transparency reports that accompany system decisions (e.g., explaining a specific credit decision) and for testing tools useful for internal and external oversight (e.g., to detect algorithmic discrimination). Distinctively, our causal QII measures carefully account for correlated inputs while measuring influence. They support a general class of transparency queries and can, in particular, explain decisions about individuals (e.g., a loan decision) and groups (e.g., disparate impact based on gender). Finally, since single inputs may not always have high influence, the QII measures also quantify the joint influence of a set of inputs (e.g., age and income) on outcomes (e.g. loan decisions) and the marginal influence of individual inputs within such a set (e.g., income). Since a single input may be part of multiple influential sets, the average marginal influence of the input is computed using principled aggregation measures, such as the Shapley value, previously applied to measure influence in voting. Further, since transparency reports could compromise privacy, we explore the transparency-privacy tradeoff and prove that a number of useful transparency reports can be made differentially private with very little addition of noise. Our empirical validation with standard machine learning algorithms demonstrates that QII measures are a useful transparency mechanism when black box access to the learning system is available. In particular, they provide better explanations than standard associative measures for a host of scenarios that we consider. Further, we show that in the situations we consider, QII is efficiently approximable and can be made differentially private while preserving accuracy.

/pdf/algorithmic-transparency-via-quantitative-input-influence-109zh4gbpl.pdf

Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems

A survey of the use of graph theoretical techniques in Biology is presented. In particular, recent work on identifying and modelling the structure of bio-molecular networks is discussed, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronisation and disease propagation.

/pdf/graph-theory-and-networks-in-biology-4xtk08o7mv.pdf

Graph theory and networks in Biology

The science of social networks is a central field of sociological study, a major application of random graph theory, and an emerging area of study by economists, statistical
physicists and computer scientists. While these literatures are (slowly) becoming aware
of each other, and on occasion drawing from one another, they are still largely distinct in
their methods, interests, and goals. Here, my aim is to provide some perspective on the
research from these literatures, with a focus on the formal modeling of social networks
and the two major types of models: those based on random graphs and those based on
game theoretic reasoning. I highlight some of the strengths, weaknesses, and potential
synergies between these two network modeling approaches.

/pdf/the-economics-of-social-networks-1vi0e7boip.pdf

The Economics of Social Networks

Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on the location of single protein, but ignored the relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures, NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the network, we calculate its edge clustering coefficient. A node's essentiality is determined by the sum of the edge clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the essential proteins discovered by NC show significant cluster effect.

Identification of Essential Proteins Based on Edge Clustering Coefficient

Centrality and power in social networks: a game theoretic approach

Centrality in directed social networks. A game theoretic approach

In this article, we generalize the position value, defined by Meessen (1988) for the class of deterministic communication situations, to the class of generalized probabilistic communication situations (G´ omez et al. (2008)). We provide two characterizations of this new allocation rule. Following in Slikker’s (2005a) footsteps, we characterize the probabilistic position value using probabilistic versions of component e ciency and balanced link contributions. Then we generalize the notion of link potential, defined by Slikker (2005b) for the class of deterministic communication situations, to the class of generalized probabilistic communication situations, and use it to characterize our allocation rule. Finally, we show that these two characterizations are logically equivalent.

https://estudiosestadisticos.ucm.es/data/cont/docs/12-2013-02-06-CT04_2009.pdf

A probabilistic position value

A value for generalized probabilistic communication situations

In this paper we define a family of centrality measures for directed social networks from a game theoretical point of view. We follow the line started with our previous work (Gomez et al.., 2003). Besides the definition, we obtain both, a characterization and an additive decomposition of the measures.

Conrado Manuel

Papers

Centrality and power in social networks: a game theoretic approach

Centrality in directed social networks. A game theoretic approach

A probabilistic position value

A value for generalized probabilistic communication situations

Centrality in Directed Social Networks: A Game Theoretic Approach