scholarly journals V–, U–, L– or W–shaped economic recovery after Covid-19: Insights from an Agent Based Model

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247823
Author(s):  
Dhruv Sharma ◽  
Jean-Philippe Bouchaud ◽  
Stanislao Gualdi ◽  
Marco Tarzia ◽  
Francesco Zamponi

We discuss the impact of a Covid-19–like shock on a simple model economy, described by the previously developed Mark-0 Agent-Based Model. We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our model economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss. This is due to the economy getting trapped in a self-sustained “bad” state. We then discuss two policies that attempt to moderate the impact of the shock: giving easy credit to firms, and the so-called helicopter money, i.e. injecting new money into the households savings. We find that both policies are effective if strong enough. We highlight the potential danger of terminating these policies too early, although inflation is substantially increased by lax access to credit. Finally, we consider the impact of a second lockdown. While we only discuss a limited number of scenarios, our model is flexible and versatile enough to accommodate a wide variety of situations, thus serving as a useful exploratory tool for a qualitative, scenario-based understanding of post-Covid recovery. The corresponding code is available on-line.

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonatan Almagor ◽  
Stefano Picascia

AbstractA contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.


2014 ◽  
Vol 104 (7) ◽  
pp. 1196-1203 ◽  
Author(s):  
Yong Yang ◽  
Ana Diez-Roux ◽  
Kelly R. Evenson ◽  
Natalie Colabianchi

Author(s):  
Juan Luis Santos ◽  
Jagoda Anna Kaszowska ◽  
Tomás Mancha Navarro

The aim of the agent-based model presented in this chapter is to explain the determinants of inflation and to forecast the inflation rate in the Eurozone for the next five years. The behaviors of agents and their expectations are interrelated and explained by macroeconomic models applied to heterogeneous agents of three classes: individuals, companies and financial institutions. In addition, the behavior of public sector and central bank is also modeled with a single agent of each kind. Once the quantitative easing policy is implemented, the quantitative theory of money expects higher inflation rates in the long run. Inflation should remain low taking into account the Phillips-Curve. Last, according to the Aggregated Supply and Demand as well as to the Money Market equilibrium, the behaviors modeled allow forecasting low inflation. However, an external shock, as it would be an increase in the price of important commodities, can alter the inflation rate to a great extent.


Author(s):  
Marija Majda Perisic ◽  
Tomislav Martinec ◽  
Mario Storga ◽  
John S Gero

AbstractThis paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.


2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Sachiko Ozawa ◽  
Daniel R. Evans ◽  
Colleen R. Higgins ◽  
Sarah K. Laing ◽  
Phyllis Awor

2019 ◽  
Vol 1343 ◽  
pp. 012143
Author(s):  
Prakhar Mehta ◽  
Danielle Griego ◽  
Alejandro Nunez-Jimenez ◽  
Arno Schlueter

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