Influential Listeners: An Experiment on Persuasion Bias in Social Networks

This paper presents an experimental investigation of persuasion bias, a form of bounded rationality whereby agents communicating through a social network are unable to account for possible repetitions in the information they receive. The results indicate that network structure plays a significant role in determining social influence. However, the most influential agents are not those with more outgoing links, as predicted by the persuasion bias hypothesis, but those with more incoming links. We show that a boundedly rational updating rule that takes into account not only agents' outdegree, but also their indegree, provides a better explanation of the experimental data. In this framework, consensus beliefs tend to be swayed towards the opinions of influential listeners. We then present an effort-weighted updating model as a more general characterization of information aggregation in social networks.

Adjustment is Much Slower than You Think

In most instances, the dynamic response of monetary and other policies to shocks is infrequent and lumpy. The same holds for the microeconomic response of some of the most important economic variables, such as investment, labor demand, and prices. We show that the standard practice of estimating the speed of adjustment of such variables with partial-adjustment ARMA procedures substantially overestimates this speed. For example, for the target federal funds rate, we find that the actual response to shocks is less than half as fast as the estimated response. For investment, labor demand and prices, the speed of adjustment inferred from aggregates of a small number of agents is likely to be close to instantaneous. While aggregating across microeconomic units reduces the bias (the limit of which is illustrated by Rotemberg's widely used linear aggregate characterization of Calvo's model of sticky prices), in some instances convergence is extremely slow. For example, even after aggregating investment across all establishments in U.S. manufacturing, the estimate of its speed of adjustment to shocks is biased upward by more than 80 percent. While the bias is not as extreme for labor demand and prices, it still remains significant at high levels of aggregation. Because the bias rises with disaggregation, findings of microeconomic adjustment that is substantially faster than aggregate adjustment are generally suspect.

Firms formation and growth in the model with heterogeneous agents and monitoring

In this article we extend the agent-based model of firms’ formation and growth proposed in [4]. In [4] the firms‘ creation, expansion or contraction results from the interaction of heterogeneous utility maximizers. While the original model was able to replicate the power law distribution in the firms’ sizes agents in the model set their utility maximizing effort levels completely freely and undetected. This led to the emergence of free riding and influenced the overall dynamics of the model. Therefore we decided to extend the original model by introducing the monitoring which is seen in the economic literature, besides for example the proper incentive scheme ([18]), as a possible way how to make employees work harder. Our motivation is to compare the extended model with both to the original case without monitoring and empirical data about firms‘ sizes distribution.monitoring, firms‘ size, power law, agent-based model, simulation,heterogeneous agents

Adjustment Is Much Slower Than You Think

In most instances, the dynamic response of monetary and other policies to shocks is infrequent and lumpy. The same holds for the microeconomic response of some of the most important economic variables, such as investment, labor demand, and prices. We show that the standard practice of estimating the speed of adjustment of such variables with partial-adjustment ARMA procedures substantially overestimates this speed. For example, for the target federal funds rate, we find that the actual response to shocks is less than half as fast as the estimated response. For investment, labor demand and prices, the speed of adjustment inferred from aggregates of a small number of agents is likely to be close to instantaneous. While aggregating across microeconomic units reduces the bias (the limit of which is illustrated by Rotemberg's widely used linear aggregate characterization of Calvo's model of sticky prices), in some instances convergence is extremely slow. For example, even after aggregating investment across all establishments in U.S. manufacturing, the estimate of its speed of adjustment to shocks is biased upward by more than 80 percent. While the bias is not as extreme for labor demand and prices, it still remains significant at high levels of aggregation. Because the bias rises with disaggregation, findings of microeconomic adjustment that is substantially faster than aggregate adjustment are generally suspect.Speed of adjustment, discrete adjustment, lumpy adjustment, aggregation, Calvo model, ARMA process, partial adjustment, expected response time, monetary policy, investment, labor demand, sticky prices, idiosyncratic shocks, impulse response function, Wold representation, time-to-build

Explaining intuitive difficulty judgments by modeling physical effort and risk

The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselves or appreciating others' accomplishments. Here we give a computational account of how humans judge the difficulty of a range of physical construction tasks (e.g., moving 10 loose blocks from their initial configuration to their target configuration, such as a vertical tower) by quantifying two key factors that influence construction difficulty: physical effort and physical risk. Physical effort captures the minimal work needed to transport all objects to their final positions, and is computed using a hybrid task-and-motion planner. Physical risk corresponds to stability of the structure, and is computed using noisy physics simulations to capture the costs for precision (e.g., attention, coordination, fine motor movements) required for success. We show that the full effort-risk model captures human estimates of difficulty and construction time better than either component alone

Fiscal Deficits, Current Account Dynamics and Monetary Policy

This paper develops a stochastic two-country "perpetual youth" Dynamic New Keynesian model of the international business cycle with incomplete international financial markets and stationary net foreign assets. The model allows for a thorough analysis of the interaction of endogenous monetary policy with endogenous, non-balanced budget fiscal policy. We derive the dynamic and cyclical properties of fiscal deficit feedback rules under alternative monetary regimes, discuss the international transmission of productivity shocks, and the implications for net foreign assets and exchange rate dynamics. Our results imply that the degree of "fiscal discipline", i.e. the extent to which the fiscal rule responds to debt dynamics, is crucial for the dynamics of net foreign assets. We show that under low fiscal discipline (characterizing most industrialized countries, first and foremost the U.S.) temporary positive productivity shocks may result in highly persistent deteriorations of the external position in the medium run. Our results also suggest that any attempt by monetary policy alone to stabilize the dynamics of net foreign assets would induce excessive and costly fluctuations of the exchange rate.Fiscal Deficit, Current Account, DSGE Models, Monetary and Fiscal Policy.

Growth and Inequality in a Small Open Economy

This paper analyzes the growth and inequality tradeoff for a small open economy where agents differ in their initial endowments of capital stock and international bond-holdings. Our analysis focuses on the distributional impacts of different structural shocks through their effects on agents’ relative wealth and their labor supply decisions. Supplementing the theoretical analysis with numerical simulations, we demonstrate that openness – access to an international capital market – has important consequences on the growth-inequality tradeoff. Specifically, the growth and distributional consequences of structural shocks depend crucially on whether the underlying heterogeneity originates with the initial endowment of domestic capital or foreign bonds.

Missing Aggregate Dynamics: On the Slow Convergence of Lumpy Adjustment Models

The dynamic response of aggregate variables to shocks is one of the central concerns of applied macroeconomics. The main measurement procedure for these dynamics consists of estimmiating an ARMA or VAR (VARs, for short). In non- or semi-structural approaches, the characterization of dynamics stops there. In other, more structural approaches, researcher try to uncover underlying adjustment cost parameters from the estimated VARs. Yet, in others, such as in RBC models, these estimates are used as the benchmark over which the success of the calibration exercise, and the need for further theorizing, is assessed. The main point of this paper is that when the microeconomic adjustment underlying the corresponding aggregates is lumpy, conventional VARs procedures are often inadequate for all of the above practices. In particular, the researcher will conclude that there is less persistence in the response of aggregate variables to aggregate shocks than there really is. Paradoxically, while idiosyncratic productivity and demand shocks smooth away microeconomic non-convexities and are often used as a justification for approximating aggregate dynamics with linear models, their presence exacerbate the bias. Since in practice idiosyncratic uncertainty is many times larger than aggregate uncertainty, we conclude that the problem of missing aggregate dynamics is prevalent in empirical and quantitative macroeconomic research.Speed of adjustment, Discrete adjustment, Lumpy adjustment, Aggregation, Calvo model, ARMA process, Partial adjustment, Expected response time, Monetary policy, Investment, Labor demand, Sticky prices, Idiosyncratic shocks, Impulse response function, Time-to-build

Predicting epidemic risk from past temporal contact data

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.Comment: 24 pages, 5 figures + SI (18 pages, 15 figures