Previous computational modeling work has shown cultural and ideological diffusion to act quickly to produce zones of homogeneous polarized cultures that have difficulty interacting with each other. Yet we do not always observe large zones of cultural homogeneity in the real world. Rather, we witness interesting variation in homo- and heterogeneity across and within different world populations and cultures. I argue that one of the key features left out of previous models of culture is the role of network structure and communication barriers in shaping the flow of information between individuals and groups. Accounting for this structure allows for the greater applicability of culture models to understanding any number of modern cultural and ideological phenomena, including ideological polarization, social communication, and cultural drift. I illustrate this argument by embedding a classic culture model (Axelrod 1997) in a more complex network structure, replicating and extending previous simulations to show that different structure does, in fact, change the equilibrium conditions of the model and make it more representative of the world we observe empirically. This finding suggests that we should not take network structure lightly in our research when examining patterns of political communication, culture, and conflict.

Experimentalists and survey researchers regularly measure the makeup and size of respondent personal discussion networks to learn about the social context in which citizens make political choices. When measuring these personal networks, some scholars use question prompts that specifically ask respondents about whom they discuss "politics" with, while others use more general prompts that ask respondents about whom they discuss "important matters" with. Prior research suggests that "political" discussion network prompts create self-reported networks that are substantively similar to "important matters" prompts. We conduct a nationally representative survey experiment to re-evaluate this question. Our results suggest that, although the size of networks generated by the two questions may be similar on average, the two questions generate different response distributions overall. In particular, respondents interested in politics report larger political discussion networks than general discussion networks, and respondents uninterested in politics report smaller political discussion networks than general discussion networks.

Abstract Objective This study investigates the degree to which social connections and social context shape attitudes and behaviors surrounding the COVID-19 pandemic in the United States. Methods In April and August 2020, we surveyed Americans about their social context and asked a range of questions related to the coronavirus and social distancing. Results Social crowding, social networks, and social context are related to support for social distancing policies and compliance with those policies. Conclusion The coronavirus pandemic created hardships;hardships made more difficult by the inability to physically interact with extended family and friends and the inability to find space away from immediate family. This research suggests that understanding compliance with public policies requires attention to interpersonal connections.

Citizens, especially in the aggregate, have historically been excellent election forecasters. This is, in part, due to discussing and hearing about the voting intentions of those around them, i.e., learning from their social networks. However, many people interact with networks that are ideological “echo chambers” made up of only likeminded voters. Does this absence of political disagreement decrease the ability of citizens to accurately predict an election result? Using a survey module from the 2015 Canadian Local Parliament Project, we examine how citizens living in partisan echo chambers fare at forecasting at the riding and parliamentary level and find that the reliability of echo chamber dwellers’ forecasts declines relative to those in more diverse environs. Our findings suggest that the role played by partisan composition of social networks is critical to understanding the accuracy and confidence of citizen electoral forecasts.

The unfunded obligations of the pension and other post employment benefits (OPEB) plans sponsored by local governments in the United States continue to grow. In the following report, we analyze the financial obligations of 21 of the 22 largest US cities. We review both their own reports on these obligations and how these differ from our estimates based on more realistic assumptions.

Based on analysis of a multi-wave national sample panel of Republican identifiers, we show the increasing coherence among rank and file Republicans around evaluations of Donald Trump. Differences between Republicans who-preferred Trump for the GOP nomination and those who preferred another-candidate (but, unlike the Never Trump group who said they could not support him in the general election if he won the nomination) are muted by the general election and 2018 waves. While "Never-Trumpers" in the nomination wave maintain their affective distance from Trump in the general election and 2018 waves, their evaluations become less negative. Our analysis suggests that Republicans' favorability toward Trump increasingly aligned with their attitudes toward the Republican Party and their support for Trump's effort to build a southern-border wall.

Much of our understanding of social influence in individual political behavior stems from representative surveys asking respondents to identify characteristics of a small number of people they talk to most frequently. By focusing only on these few close contacts, we have implicitly assumed that less-intimate associates and features of network structure hold little influence over others' attitudes and behavior. We test these assumptions with a survey that attempted to interview all students at a small university during a highly-salient municipal election. By focusing on a small, well-defined community, we are able to explore the relationship between individuals, their close associates, and also less-immediate associates. We are also able to explore features of network structure unobtainable in representative samples. We demonstrate that these less-immediate associates and network features have the potential to exert important influence that conventional survey approaches would miss.

Interpersonal social networks play a critical role in increasing turnout through voter cooperation and coordination. However, different contexts and institutions can inhibit this type of coordination by increasing voter uncertainty. We examine one mechanism by which third party entry in plurality voting systems might decrease turnout by increasing voter confusion about the political preferences of their fellow citizens. We extend the Fowler Turnout in a Small World model [18], providing theoretical evidence that voters may be disincentivized to turn out under three party competition in single member district plurality systems. Finally, we support our theoretical work with an examination of turnout in modern British elections, which provides empirical support for our theoretical findings.

One of the focal points of social networks research has been the process by which individuals utilize information and cues from their social networks and communities to form political attitudes and make decisions about how and when to participate in politics. Not all individuals, however, have large social networks or are strongly connected to their local social environments. Furthermore, despite concerns about rising social isolation in American society, the role that relatively socially disconnected individuals play in politics is not well understood. Using a nationally representative data set with information about communities, social networks, and individual-level variables, this paper examines social connectedness and political behavior. Those who are more socially isolated, it is found, are neither more conservative nor liberal on any particular political issues, but clearly participate in politics less than individuals who are well connected to those around them. Finally, while individual political ideology is not correlated with isolation, the contextual influence of the local environment on individual preferences is correlated with social connectedness. When compared with well connected citizens, individuals who are more isolated are less likely to have their vote choices influenced by those around them. Individual social connectedness conditions the effect of contextual social influence.

This article presents a study on freeway networks instrumented with coordinated ramp metering and the ability of such control systems to produce arbitrarily complex congestion patterns within the dynamical limits of the traffic system. The developed method is used to evaluate the potential for an adversary with access to control infrastructure to enact high-level attacks on the underlying freeway system. The attacks are executed using a predictive, coordinated ramp metering controller based on finite-horizon optimal control and multi-objective optimization techniques. The efficacy of the control schemes in carrying out the prescribed attacks is determined via simulations of traffic network models based on the cell transmission model with onramps modeled as queue buffers. Freeway attacks with high-level objectives are presented on two illustrative examples: congestion on-demand, which aims to create precise, user-specified pockets of congestion, and catch me-if-you-can, which attempts to aid a fleeing vehicle from pursuant vehicles. (C) 2016 Elsevier Ltd. All rights reserved.

The adjoint method provides a computationally efficient means of calculating the gradient for applications in constrained optimization. In this article, we consider a network of scalar conservation laws with general topology, whose behavior is modified by a set of control parameters in order to minimize a given objective function. After discretizing the corresponding partial differential equation models via the Godunov scheme, we detail the computation of the gradient of the discretized system with respect to the control parameters and show that the complexity of its computation scales linearly with the number of discrete state variables for networks of small vertex degree. The method is applied to the problem of coordinated ramp metering on freeway networks. Numerical simulations on the I15 freeway in California demonstrate an improvement in performance and running time compared with existing methods. In the context of model predictive control, the algorithm is shown to be robust to noise in the initial data and boundary conditions.

Optimal control problems on dynamical systems are concerned with finding a control policy, which minimizes a desired objective, where the objective value depends on the future evolution of the system ( the state of the system), which, in turn, depends on the control policy. For systems which contain subsystems that are disjoint across the state variables, distributed optimization techniques exist, which iteratively update subsystems concurrently and then exchange information between subsystems with shared control variables. This article presents a method, based on the asynchronous alternating directions method of multiplier algorithm, which extends these techniques to subsystems with shared control and state variables, while maintaining similar communication structure. The method is used as the basis for splitting network flow control problems into many subnetwork control problems with shared boundary conditions. The decentralized and parallel nature of the method permits high scalability with respect to the size of the network. For highly nonconvex applications, an efficient method, based on adjoint gradient computations, is presented for solving subproblems with shared state. The method is applied to decentralized, coordinated ramp metering and variable speed limit control on a realistic freeway network model using distributed model predictive control.

This article focuses on cybersecurity of transportation systems and investigates their vulnerability to attacks on the sensing and control infrastructure. An array of different attack points, classified into physical, close-proximity, and virtual layers, are reviewed and investigated. The authors construct two benchmark scenarios which exploit these vulnerabilities to identify the potential harm of a traffic control system compromise. A more in-depth analysis is then presented on the takeover of a series of networked onramp metering traffic lights. The analysis is conducted using a methodology for precise and intelligent onramp metering attacks based on finite-horizon optimal control techniques and multi-objective optimization. The methodology is demonstrated in simulation for two examples of high-level attack objectives: congestion-on-demand, which aims to create precise pockets of congestion on a model of the I15 San Diego freeway, and catch-me-if-you-can, which attempts to aid a fleeing vehicle from chasing pursuants.

We consider the System Optimal Dynamic Traffic Assignment problem with Partial Control (SO-DTA-PC) for general networks with horizontal queuing. The goal of which is to optimally control any subset of the networks agents to minimize the total congestion of all agents in the network. We adopt a flow dynamics model that is a Godunov discretization of the Lighthill-Williams-Richards (LWR) partial differential equation with a triangular flux function and a corresponding multi-commodity junction solver. Full Lagrangian paths are assumed to be known for the controllable agents, while we only assume knowledge of the aggregate split ratios for the non-controllable (selfish) agents. We solve the resulting finite horizon non-linear optimal control problem using the discrete adjoint method.

We consider the Lighthill-Whitham-Richards traffic flow model on a junction composed by one mainline, an onramp, and an offramp, which are connected by a node. The onramp dynamics is modeled using an ordinary differential equation describing the evolution of the queue length. The definition of the solution of the Riemann problem at the junction is based on an optimization problem and the use of a right-of-way parameter. The numerical approximation is carried out using a Godunov scheme, modified to take into account the effects of the onramp buffer. We present the result of some simulations and numerically check the convergence of the method.