Opinion dynamics with varying susceptibility to persuasion

A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion. In this work, we adopt a popular model for social opinion dynamics, and we formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility lead to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization and show its efficacy at finding target-sets that optimize the sum of opinions compared on real world networks, including a Twitter network with real opinion estimates

Glance and Focus Networks for Dynamic Visual Recognition

Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models which process all the pixels with an equal amount of computation result in considerable redundancy in terms of time and space consumption. In this paper, we formulate the image recognition problem as a sequential coarse-to-fine feature learning process, mimicking the human visual system. Specifically, the proposed Glance and Focus Network (GFNet) first extracts a quick global representation of the input image at a low resolution scale, and then strategically attends to a series of salient (small) regions to learn finer features. The sequential process naturally facilitates adaptive inference at test time, as it can be terminated once the model is sufficiently confident about its prediction, avoiding further redundant computation. It is worth noting that the problem of locating discriminant regions in our model is formulated as a reinforcement learning task, thus requiring no additional manual annotations other than classification labels. GFNet is general and flexible as it is compatible with any off-the-shelf backbone models (such as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the feature extractor. Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). Journal version of arXiv:2010.05300 (NeurIPS 2020). The first two authors contributed equall

To Be or Not To Be in Office Again, That is the Question: Political Business Cycles with Local Governments

Most opportunistic -type models of political business cycles tend to posit a given objective for incumbents: maximisation of re-election chances. Though taking an opportunistic view too, we suggest a new explanation for a fiscal policy cycle: the incumbents concern with her own welfare in cases of victory and defeat. This rationale addresses local policy-making in particular. An equilibrium perfectforesight model is designed which totally dispenses with any form of irrationality (namely, on the part of voters) or the common objective functions (re-election chances). Being well grounded in basic microeconomic theory (welfare maximisation by the individual agent), our model provides another foundation for the emergence of political business cycles at the local level. The empirical plausibility of theoretical predictions is then tested on Portuguese municipal data ranging from 1977 to 1993. The estimation of an error-components econometric framework finds evidence in favour of the proposed explanation and enlightens the role played by several politicoeconomic determinants of local governments investment outlays, such as electoral calendar, re-candidacy decisions, political cohesion and intergovernmental capital transfers.local public finance; public choice; political business cycles; elections; Portugal