scholarly journals The Role of Chance in Fencing Tournaments: an Agent-Based Approach

2022 ◽  
Author(s):  
Chiara Zappalà ◽  
Alessandro Pluchino ◽  
Andrea Rapisarda ◽  
Alessio Emanuele Biondo ◽  
Pawel Sobkowicz
Keyword(s):  
Relative Weight ◽  
Extreme Case ◽  
Real Data ◽  
Free Variable ◽  
Agent Based ◽  
Entire Career ◽  
Widespread Belief ◽  
Winner Takes All ◽  
Individual Sports

Abstract It is a widespread belief that success is mainly due to innate qualities, rather than to external forces.This is particularly true in sport competitions, where individual talent is usually considered the main, if not the only, ingredient in order to reach success. In this study, with the help of both real data and agent-based simulations, we explore the limits of this belief by quantifying the relative weight of talent and chance in fencing, a combat sport involving a weapon. Fencing competitions are structured as direct elimination tournaments, where randomness is explicitly present in some rules. Our dataset covers the last decade of international events and consists of both single competition results and annual rankings for male and female fencers under 20 years old (Junior category). Our model is calibrated on the dataset and parametrized by just one free variable 'a' describing the importance of talent - and, consequently, of chance - in competitions (a = 1 indicates the ideal scenario where only talent matters, a = 0 the complete random one). Our agent-based approach is able to reproduce the main stylized facts observed in real data, at the level of both single fencing tournaments and entire careers of a given community of fencers. We find that simulations approximate very well the real data for both Junior Men and Women when talent weights slightly less than chance, i.e. when 'a' is around 0.45. We conclude that the role of chance in fencing is unusually high and it probably represents an extreme case for individual sports. Our results shed light on the importance of external factors in both athletes' results in single tournaments and their entire career, making even more unfair the ``winner-takes-all'' disparities in remuneration which often occur among the winner and the other classified.

scholarly journals The Role of Chance in Individual Sports: an Agent-Based Approach for Fencing Tournaments

2021 ◽  
Author(s):  
Chiara Zappalà ◽  
Alessandro Pluchino ◽  
Andrea Rapisarda ◽  
Alessio Emanuele Biondo ◽  
Pawel Sobkowicz
Keyword(s):  
Relative Weight ◽  
Extreme Case ◽  
Real Data ◽  
Free Variable ◽  
Agent Based ◽  
Entire Career ◽  
Winner Takes All ◽  
External Forces ◽  
Individual Sports

Abstract It is a widespread belief that success is mainly due to innate qualities, rather than to external forces. This is particularly true in sport competitions, where individual talent is considered the only ingredient in order to reach success. In this study, we propose to explore the relative weight of talent and luck in individual sports through agent-based models. In particular, we chose fencing as case study, that is a combat sport involving a weapon. Fencing competitions are structured as direct elimination tournaments, where randomness is explicitly present in some rules. Our dataset covers the last decade of international events and consists of both single competition results and annual rankings for male and female fencers under 20 years old (Junior category). We show that our agent-based approach, calibrated on the dataset and parametrized by just one free variable a describing the importance of talent in competitions (a = 1 indicates the ideal scenario where only talent matters, a = 0 the complete random one) is able to reproduce the main stylized facts observed in real data, both at the level of single fencing tournaments and of entire careers of a given community of fencers. We find that simulations approximate very well the real data when talent weights slightly more than luck, i.e. when a is around 0.55 for Junior Men, or even slightly less than luck, i.e. when a = 0.45 for Junior Women. We conclude that the role of chance in fencing is highly underestimated even if probably it represents an extreme case for individual sports. Our results shed light on the importance of external factors in both athletes’ results in single tournaments and in their entire career, making even more unfair the “winner-takes-all” disparities in remuneration.

scholarly journals Exploring the dynamics of neighborhood ethnic segregation with agent-based modelling: an empirical application to Bradford

2021 ◽  
Author(s):  
Carolina Zuccotti ◽  
Jan Lorenz ◽  
Rocco Paolillo ◽  
Alejandra Rodríguez Sánchez ◽  
Selamavit Serka
Keyword(s):  
Real Data ◽  
Agent Based Modeling ◽  
Residential Choice ◽  
Ethnic Segregation ◽  
Agent Based ◽  
Schelling Model ◽  
Residential Moves ◽  
Micro Level ◽  
The Uk

How individuals’ residential moves in space—derived from their varied preferences and constraints—translate into the overall segregation patterns that we observe, remains a key challenge in neighborhood ethnic segregation research. In this paper we use agent-based modeling to explore this concern, focusing on the interactive role of ethnic and socio-economic homophily preferences and housing constraints as determinants of residential choice. Specifically, we extend the notorious Schelling’s model to a random utility discrete choice approach to simulate the relocation decision of people (micro level) and how they translate into spatial segregation outcomes (macro level). We model different weights for preferences of ethnic and socioeconomic similarity in neighborhood composition over random relocations, in addition to housing constraints. We formalize how different combinations of these variables could replicate real segregation scenarios in Bradford, a substantially segregated local authority in the UK. We initialize our model with geo-referenced data from the 2011 Census and use Dissimilarity and the Average Local Simpson Indices as measures of segregation. As in the original Schelling model, the simulation shows that even mild preferences to reside close to co-ethnics can lead to high segregation levels. Nevertheless, ethnic over-segregation decreases, and results come close to real data, when preferences for socioeconomic similarity are slightly above preferences for ethnic similarity, and even more so when housing constraints are considered in relocation moves of agents. We discuss the theoretical and policy contributions of our work.

scholarly journals THE ROLE OF TASTE AFFINITY IN AGENT-BASED MODELS FOR SOCIAL RECOMMENDATION

2013 ◽  
Vol 16 (04n05) ◽  
pp. 1350009 ◽  
Author(s):  
GIULIO CIMINI ◽  
AN ZENG ◽  
MATÚŠ MEDO ◽  
DUANBING CHEN
Keyword(s):  
Main Tool ◽  
Real Data ◽  
Similarity Metrics ◽  
Adaptive Model ◽  
Social Recommendation ◽  
Agent Based ◽  
Online Social Media ◽  
Realistic Assumption ◽  
The Given

In the Internet era, online social media emerged as the main tool for sharing opinions and information among individuals. In this work, we study an adaptive model of a social network where directed links connect users with similar tastes, and over which information propagates through social recommendation. Agent-based simulations of two different artificial settings for modeling user tastes are compared with patterns seen in real data, suggesting that users differing in their scope of interests is a more realistic assumption than users differing only in their particular interests. We further introduce an extensive set of similarity metrics based on users' past assessments, and evaluate their use in the given social recommendation model with both artificial simulations and real data. Superior recommendation performance is observed for similarity metrics that give preference to users with small scope — who thus act as selective filters in social recommendation.

EVALUATION OF EFFECTS OF COLLABORATIVE PATTERNS ON THE EFFICIENCY OF SCIENTIFIC NETWORKS USING SIMULATION

2018 ◽  
Vol 22 (04) ◽  
pp. 1850035
Author(s):  
DORSA TAJADDOD ALIZADEH ◽  
ANDREA SCHIFFAUEROVA
Keyword(s):  
Simulation Model ◽  
Knowledge Creation ◽  
Positive Impact ◽  
Real Data ◽  
Negative Effects ◽  
Agent Based Simulation ◽  
Collaborative Relationships ◽  
Agent Based ◽  
Overall Efficiency

The objective of this work is to investigate the role of individual scientists and their collaborations in knowledge creation networks. In order to study the networks in their dynamic context, an agent-based simulation model is developed using real data based on the Canadian biotechnology publications. We observe that while the repetitiveness of the collaborative relationships among scientists shows negative effects, the presence of the gatekeepers is found to be critical for the overall efficiency of the network. We also find positive impact of star scientists on the network productivity, but their negative effects on the flow of knowledge are detected as well.

scholarly journals A Stochastic Kinetic Type Reactions Model for COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1221
Author(s):  
Giorgio Sonnino ◽  
Fernando Mora ◽  
Pasquale Nardone
Keyword(s):  
Real Data ◽  
Stochastic Noise ◽  
Mathematical Basis ◽  
Sis Model ◽  
Theoretical Predictions ◽  
Coronavirus Infection ◽  
Massive Distribution ◽  
Second Wave ◽  
Stochastic Sis Model

We propose two stochastic models for the Coronavirus pandemic. The statistical properties of the models, in particular the correlation functions and the probability density functions, were duly computed. Our models take into account the adoption of lockdown measures as well as the crucial role of hospitals and health care institutes. To accomplish this work we adopt a kinetic-type reaction approach where the modelling of the lockdown measures is obtained by introducing a new mathematical basis and the intensity of the stochastic noise is derived by statistical mechanics. We analysed two scenarios: the stochastic SIS-model (Susceptible ⇒ Infectious ⇒ Susceptible) and the stochastic SIS-model integrated with the action of the hospitals; both models take into account the lockdown measures. We show that, for the case of the stochastic SIS-model, once the lockdown measures are removed, the Coronavirus infection will start growing again. However, the combined contributions of lockdown measures with the action of hospitals and health institutes is able to contain and even to dampen the spread of the SARS-CoV-2 epidemic. This result may be used during a period of time when the massive distribution of vaccines in a given population is not yet feasible. We analysed data for USA and France. In the case of USA, we analysed the following situations: USA is subjected to the first wave of infection by Coronavirus and USA is in the second wave of SARS-CoV-2 infection. The agreement between theoretical predictions and real data confirms the validity of our approach.

scholarly journals Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua
Keyword(s):  
Time Series ◽  
Default Risk ◽  
Real Life ◽  
Statistical Error ◽  
Real Data ◽  
Sampling Period ◽  
Calibration Procedure ◽  
Industry Standards ◽  
Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

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.

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