Obvious strategyproofness needs monitoring for good approximations (extended abstract)

Obvious strategyproofness (OSP) is an appealing concept as it allows to maintain incentive compatibility even in the presence of agents that are not fully rational, e.g., those who struggle with contingent reasoning [10]. However, it has been shown to impose some limitations, e.g., no OSP mechanism can return a stable matching [3] . We here deepen the study of the limitations of OSP mechanisms by look-ing at their approximation guarantees for basic optimization problems paradigmatic of the area, i.e., machine scheduling and facility location. We prove a number of bounds on the approximation guarantee of OSP mechanisms, which show that OSP can come at a signifificant cost. How-ever, rather surprisingly, we prove that OSP mechanisms can return opti-mal solutions when they use monitoring|a mechanism design paradigm that introduces a mild level of scrutiny on agents' declarations [9]

Obvious strategyproofness needs monitoring for good approximations

Obvious strategyproofness (OSP) is an appealing concept as it allows to maintain incentive compatibility even in the presence of agents that are not fully rational, e.g., those who struggle with contingent reasoning (Li 2015). However, it has been shown to impose some limitations, e.g., no OSP mechanism can return a stable matching (Ashlagi and Gonczarowski 2015). We here deepen the study of the limitations of OSP mechanisms by looking at their approximation guarantees for basic optimization problems paradigmatic of the area, i.e., machine scheduling and facility location. We prove a number of bounds on the approximation guarantee of OSP mechanisms, which show that OSP can come at a significant cost. However, rather surprisingly, we prove that OSP mechanisms can return optimal solutions when they use monitoring?a novel mechanism design paradigm that introduces a mild level of scrutiny on agents? declarations (Kovács, Meyer, and Ventre 2015)

Social Pressure in Opinion Games

Motivated by privacy and security concerns in online social networks, we study the role of social pressure in opinion games. These are games, important in economics and sociology, that model the formation of opinions in a social network. We enrich the definition of (noisy) best-response dynamics for opinion games by introducing the pressure, increasing with time, to reach an agreement. We prove that for clique social networks, the dynamics always converges to consensus (no matter the level of noise) if the social pressure is high enough. Moreover, we provide (tight) bounds on the speed of convergence; these bounds are polynomial in the number of players provided that the pressure grows sufficiently fast. We finally look beyond cliques: we characterize the graphs for which consensus is guaranteed, and make some considerations on the computational complexity of checking whether a graph satisfies such a condition

Update on the role of elastography in liver disease

The diagnosis of liver fibrosis and the assessment of its severity are important to provide appropriate management, to determine the prognosis or the need for surveillance. Currently, for fibrosis staging, liver stiffness measurement (LSM) with the shear wave elastography (SWE) techniques is considered a reliable substitute for liver biopsy in several clinical scenarios. Nonetheless, it should be emphasized that stiffness value is a biomarker of diffuse liver disease that must be interpreted taking into consideration anamnesis, clinical and laboratory data. In patients with diffuse liver disease, it is more clinically relevant to determine the likelihood of advanced disease rather than to obtain an exact stage of liver fibrosis using a histologic classification. In this regard, a ‘rule of five’ for LSMs with vibration-controlled transient elastography (VCTE) and a ‘rule of four’ for LSMs with the acoustic radiation force impulse (ARFI)-based techniques have been proposed. In patients with advanced chronic liver disease (CLD), the risk of liver decompensation increases with increasing liver stiffness value. SWE has been proposed as a tool to predict the risk of death or complications in patients with CLD. LSM by VCTE combined with platelets count is a validated non-invasive method for varices screening, with very good results in terms of invasive procedures being spared. ARFI-based techniques also show some promising results in this setting. LSM, alone or combined in scores or algorithms with other parameters, is used to evaluate the risk of hepatocellular carcinoma occurrence. Due to the high prevalence of CLD, screening the population at risk is of interest but further studies are needed

Nonlocal interpretation of λ\lambda-variational symmetry-reduction method

In this paper we give a geometric interpretation of a reduction method based on the so called $\lambda$-variational symmetry (C. Muriel, J.L. Romero and P. Olver 2006 \emph{Variational $C^{\infty}$-symmetries and Euler-Lagrange equations} J. Differential equations \textbf{222} 164-184). In general this allows only a partial reduction but it is particularly suitable for the reduction of variational ODEs with a lack of computable local symmetries. We show that this method is better understood as a nonlocal symmetry-reduction

On the geometry of twisted symmetries: Gauging and coverings

We consider the theory of twisted symmetries of differential equations, in particular \u3bb and \u3bc-symmetries, and discuss their geometrical content. We focus on their interpretation in terms of gauge transformations on the one hand, and of coverings on the other one

On Augmented Stochastic Submodular Optimization: Adaptivity, Multi-Rounds, Budgeted, and Robustness

In this work we consider the problem of Stochastic Submodular Maximization, in which we would like to maximize the value of a monotone and submodular objective function, subject to the fact that the values of this function depend on the realization of stochastic events. This problem has applications in several areas, and in particular it well models basic problems such as influence maximization and stochastic probing. In this work, we advocate the necessity to extend the study of this problem in order to include several different features such as a budget constraint on the number of observations, the chance of adaptively choosing what we observe or the presence of multiple rounds. We here speculate on the possible directions that this line of research can take. In particular, we will discuss about interesting open problems mainly in the settings of robust optimization and online learning

General Opinion Formation Games with Social Group Membership (Short Paper)

Modeling how agents form their opinions is of paramount importance for designing marketing and electoral campaigns. In this work, we present a new framework for opinion formation which generalizes the well-known Friedkin-Johnsen model by incorporating three important features: (i) social group membership, that limits the amount of influence that people not belonging to the same group may lead on a given agent; (ii) both attraction among friends, and repulsion among enemies; (iii) different strengths of influence lead from different people on a given agent, even if the social relationships among them are the same. We show that, despite its generality, our model always admits a pure Nash equilibrium which, under opportune mild conditions, is even unique. Next, we analyze the performances of these equilibria with respect to a social objective function defined as a convex combination, parametrized by a value λ ∈ [0, 1], of the costs yielded by the untruthfulness of the declared opinions and the total cost of social pressure. We prove bounds on both the price of anarchy and the price of stability which show that, for not-too-extreme values of λ, performance at equilibrium are very close to optimal ones. For instance, in several interesting scenarios, the prices of anarchy and stability are both equal to (Equation presented) which never exceeds 2 for λ ∈ [1/5, 1/2]

Minority Becomes Majority in Social Networks

It is often observed that agents tend to imitate the behavior of their neighbors in a social network. This imitating behavior might lead to the strategic decision of adopting a public behavior that differs from what the agent believes is the right one and this can subvert the behavior of the population as a whole. In this paper, we consider the case in which agents express preferences over two alternatives and model social pressure with the majority dynamics: at each step an agent is selected and its preference is replaced by the majority of the preferences of her neighbors. In case of a tie, the agent does not change her current preference. A profile of the agents' preferences is stable if the preference of each agent coincides with the preference of at least half of the neighbors (thus, the system is in equilibrium). We ask whether there are network topologies that are robust to social pressure. That is, we ask if there are graphs in which the majority of preferences in an initial profile always coincides with the majority of the preference in all stable profiles reachable from that profile. We completely characterize the graphs with this robustness property by showing that this is possible only if the graph has no edge or is a clique or very close to a clique. In other words, except for this handful of graphs, every graph admits at least one initial profile of preferences in which the majority dynamics can subvert the initial majority. We also show that deciding whether a graph admits a minority that becomes majority is NP-hard when the minority size is at most 1/4-th of the social network size.Comment: To appear in WINE 201