scholarly journals Does START-UP NY Promote Firm Formation?

2021 ◽  
Vol 20 ◽  
pp. 105
Author(s):  
Woosung Kim
Keyword(s):  
New York ◽  
Causal Inference ◽  
Local Economy ◽  
Agent Based Model ◽  
Firm Formation ◽  
Agent Based ◽  
Tax Exemptions ◽  
Start Up ◽  
Simulation Results ◽  
The Impact

This paper explores the impact of START-UP NY policy throughout simulations by using the agent-based model. In 2013, Governor Cuomo introduced the policy START-UP NY (New York), designed to create more jobs by helping people start or move their qualified businesses into tax-free zones. Measuring the impact of START-UP NY, however, is difficult because the data are not yet available for causal inference purposes. The agent-based model developed for this paper is designed to simulate the impact of START-UP NY on the local economy of Tompkins County by conducting the Cobb-Douglas function into the model using the data from the IMPLAN model. The simulation results show that ensuring a stable demand for firms’ output is more critical for firms to survive than the kind of tax exemptions offered by START-UP NY. 

scholarly journals Why a Globally Fair COVID-19 Vaccination? An Analysis based on Agent-Based Simulation

2021 ◽  
Author(s):  
Kashif Zia
Keyword(s):  
Transmission Model ◽  
Agent Based Model ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Simulation Results ◽  
Time Required ◽  
The Impact ◽  
Spatial Transmission

In this paper, an Agent-Based Model (ABM) is proposed to evaluate the impact of COVID-19 vaccination drive in different settings. The main focus is to evaluate the counter-effectiveness of disparity in vaccination drive among different regions/countries. The model proposed is simple yet novel in the sense that it captures the spatial transmission-induced activity into consideration, through which we are able to relate the transmission model to the mutated variations of the virus. Some important what-if questions are asked in terms of the number of deaths, and time required and the percentage of the population needed to be vaccinated before the pandemic is eradicated. The simulation results have revealed that it is necessary to maintain a global (rather than regional or country-oriented) vaccination drive in case of a new pandemic or continual efforts against COVID-19.

scholarly journals Lives saved and hospitalizations averted by COVID-19 vaccination in New York City

2021 ◽  
Author(s):  
Affan Shoukat ◽  
Thomas Nogueira Vilches ◽  
Seyed Moghadas ◽  
Pratha Sah ◽  
Eric C Schneider ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Vaccine Coverage ◽  
Vaccination Campaign ◽  
Agent Based Model ◽  
Agent Based ◽  
The Impact ◽  
The City ◽  
Sustained Increase

Despite the emergence of highly transmissible variants, the number of cases in NYC has fallen from over 5,500 average daily cases in January, 2020 to less than 350 average daily cases in July, 2021. The impact of vaccination in saving lives and averting hospitalizations in NYC has not been formally investigated yet. We used an age-stratified agent-based model calibrated to COVID-19 transmission and vaccination in NYC to evaluate the impact of the vaccination campaign in suppressing the COVID-19 burden. We found that the vaccination campaign has prevented over 250,000 COVID-19 cases, 44,000 hospitalizations and 8,300 deaths from COVID-19 infection since the start of vaccination through July 1, 2021. Notably, the swift vaccine rollout suppressed another wave of COVID-19 that would have led to sustained increase in cases, hospitalizations and deaths during spring triggered by highly transmissible variants. As the Delta variant sweeps across the city, the findings of this study underscore the urgent need to accelerate vaccination and close the vaccine coverage gaps across the city.

scholarly journals Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonatan Almagor ◽  
Stefano Picascia
Keyword(s):  
High Rate ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Agent Based ◽  
The Social ◽  
Behavioural Factors ◽  
The Impact ◽  
Different Levels ◽  
User Rate

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.

scholarly journals Examining the Impact of the Walking School Bus With an Agent-Based Model

2014 ◽  
Vol 104 (7) ◽  
pp. 1196-1203 ◽  
Author(s):  
Yong Yang ◽  
Ana Diez-Roux ◽  
Kelly R. Evenson ◽  
Natalie Colabianchi
Keyword(s):  
Agent Based Model ◽  
School Bus ◽  
Agent Based ◽  
Walking School Bus ◽  
The Impact

Learning by failure vs learning by habits

2017 ◽  
Vol 23 (3) ◽  
pp. 524-546 ◽  
Author(s):  
Lorella Cannavacciuolo ◽  
Luca Iandoli ◽  
Cristina Ponsiglione ◽  
Giuseppe Zollo
Keyword(s):  
Learning Strategies ◽  
Diffusion Of Innovation ◽  
System Level ◽  
Alternative View ◽  
Agent Based Model ◽  
Content Type ◽  
Agent Based ◽  
Business Transactions ◽  
Virtual Lab ◽  
Start Up

Purpose The purpose of this paper is to explain the emergence of collaboration networks in entrepreneurial clusters as determined by the way entrepreneurs exchange knowledge and learn through business transactions needed to implement temporary supply chains in networks of co-located firms. Design/methodology/approach A socio-computational approach is adopted to model business transactions and supply chain formation in Marshallian industrial districts (IDs). An agent-based model is presented and used as a virtual lab to test the hypotheses between the firms’ behaviour and the emergence of structural properties at the system level. Findings The simulation findings and their validation based on the comparison with a real world cluster show that the topological properties of the emerging network are influenced by the learning strategies and decision-making criteria firms use when choosing partners. With reference to the specific case of Marshallian IDs it is shown that inertial learning based on history and past collaboration represents in the long term a major impediment for the emergence of hubs and of a network topology that is more conducive to innovation and growth. Research limitations/implications The paper offers an alternative view of entrepreneurial learning (EL) as opposed to the dominant view in which learning occurs as a result of exceptional circumstances (e.g. failure). The results presented in this work show that adaptive, situated, and day-by-day learning has a profound impact on the performance of entrepreneurial clusters. These results are encouraging to motivate additional research in areas such as in modelling learning or in the application of the proposed approach to the analysis of other types of entrepreneurial ecosystems, such as start-up networks and makers’ communities. Practical implications Agent-based model can support policymakers in identifying situated factors that can be leveraged to produce changes at the macro-level through the identification of suitable incentives and social networks re-engineering. Originality/value The paper presents a novel perspective on EL and offers evidence that micro-learning strategies adopted and developed in routine business transactions do have an impact on firms’ performances (survival and growth) as well as on systemic performances related to the creation and diffusion of innovation in firms networks.

Endogenous Uncertainty and the Dynamics of Constraints

2019 ◽  
pp. 246-260
Author(s):  
Paul Humphreys
Keyword(s):  
Utility Maximization ◽  
Social Dynamics ◽  
Fitness Landscape ◽  
Agent Based Model ◽  
Agent Based ◽  
Endogenous Uncertainty ◽  
The Commons ◽  
Simulation Results ◽  
Special Case

An agent- based model of social dynamics is introduced using a deformable fitness landscape, and it is shown that in certain clearly specifiable situations, strategies that are different from utility maximization outperform utility maximizers. Simulation results are presented and intuitive interpretations of the results provided. The situations considered occur when individuals' actions affect the outcomes for other agents and endogenous effects are dominant. The Tragedy of the Commons is merely a special case of this. Arguments are given that constraints are to be encouraged in some circumstances. The appropriate role of constraints in various types of society is assessed and their use justified in identifiable types of situations.

scholarly journals A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

2019 ◽  
Vol 1 (1) ◽  
pp. 11-20
Author(s):  
Marija Majda Perisic ◽  
Tomislav Martinec ◽  
Mario Storga ◽  
John S Gero
Keyword(s):  
Computational Study ◽  
Space Exploration ◽  
Solution Space ◽  
Problem Solution ◽  
Design Team ◽  
Design Teams ◽  
Agent Based Model ◽  
Problem Space ◽  
Agent Based ◽  
The Impact

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.

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