On the Interplay between Extortion and Punishment. An Agent Based Model of "Camorra"

2013 ◽  
pp. 65-77
Author(s):  
Barbara Sonzogni ◽  
Federico Cecconi ◽  
Rosaria Conte
Keyword(s):  
Social Learning ◽  
Agent Based Model ◽  
Learning Mechanisms ◽  
Italian Region ◽  
Agent Based ◽  
Preliminary Results ◽  
Highly Active

This paper presents an Agent-Based Model aimed to reproduce the demographics, economic and employment variables of a Southern Italian region (Campania) where one specific variant of Extortion Racketeering Systems (Erss), camorra, is highly active and prosperous. Preliminary results of a set of simulations show the effects of varying levels of extortion and punishment on the rates of inactivity, employment, etc. of a population of agents endowed with social learning mechanisms

scholarly journals An Agent Based Model to Detect Variations in the Transmission of Potters from Baetica Province

2021 ◽  
Author(s):  
Maria Coto-Sarmiento ◽  
Simon Carrignon
Keyword(s):  
Roman Empire ◽  
Social Learning ◽  
Material Culture ◽  
Learning Strategies ◽  
Large Scale ◽  
Scale Production ◽  
Agent Based Model ◽  
Pottery Production ◽  
Agent Based ◽  
Modes Of Transmission

The goal of this study is to analyse the transmission of technical skills among potters within the Roman Empire. Specifically, our case study has been focused on the production processes based on Baetica province (currently Andalusia) from 1st to 3rd century AD. Variability of material culture allows observing different production patterns that can explain how social learning evolves. Some differences can be detected in the making techniques processes through time and space that might explain different degrees of specialization. Unfortunately, it is extremely difficult to identify some evidence of social learning strategies in the archaeological record. In Archaeology, this process has been analysed by the study of the production of handmade pottery. In our case, we want to know if the modes of transmission could be similar with a more standardized production as Roman Age. We propose here an Agent-Based Model to compare different cultural processes of learning transmission. Archaeological evidence will be used to design the model. In this model, we implement a simple mechanism of pottery production with different social learning processes under different scenarios. In particular, the aim of this study is to quantify which one of those processes explain better the copying mechanisms among potters revealed in our dataset. We believe that the model presented here can provide a strong baseline for the exploration of transmission processes related to large-scale production.

scholarly journals Copy-when-uncertain: bumblebees rely on social information when rewards are highly variable

2016 ◽  
Vol 12 (6) ◽  
pp. 20160188 ◽  
Author(s):  
Marco Smolla ◽  
Sylvain Alem ◽  
Lars Chittka ◽  
Susanne Shultz
Keyword(s):  
Social Learning ◽  
Personal Information ◽  
Social Information ◽  
Information Use ◽  
Social Cues ◽  
Agent Based Model ◽  
Agent Based ◽  
Variable Environments ◽  
Foraging Decisions

To understand the relative benefits of social and personal information use in foraging decisions, we developed an agent-based model of social learning that predicts social information should be more adaptive where resources are highly variable and personal information where resources vary little. We tested our predictions with bumblebees and found that foragers relied more on social information when resources were variable than when they were not. We then investigated whether socially salient cues are used preferentially over non-social ones in variable environments. Although bees clearly used social cues in highly variable environments, under the same conditions they did not use non-social cues. These results suggest that bumblebees use a ‘copy-when-uncertain’ strategy.

A General Agent-Based Model of Social Learning

2017 ◽  
Author(s):  
Sarah Nowak ◽  
Luke Matthews ◽  
Andrew Parker
Keyword(s):  
Social Learning ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Network-based diffusion analysis: a new method for detecting social learning

2009 ◽  
Vol 276 (1663) ◽  
pp. 1829-1836 ◽  
Author(s):  
Mathias Franz ◽  
Charles L. Nunn
Keyword(s):  
Social Learning ◽  
Likelihood Estimation ◽  
Information Criterion ◽  
Learning Mechanisms ◽  
Agent Based ◽  
Social Network Structure ◽  
Diffusion Analysis ◽  
Wide Range ◽  
Diffusion Curve ◽  
Group Members

Social learning has been documented in a wide diversity of animals. In free-living animals, however, it has been difficult to discern whether animals learn socially by observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyses the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then be identified using model selection based on the Akaike information criterion. We tested our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison with diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.

scholarly journals Inferring mechanisms of response prioritization on social media under information overload

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chathika Gunaratne ◽  
William Rand ◽  
Ivan Garibay
Keyword(s):  
Social Media ◽  
Information Overload ◽  
User Interaction ◽  
Evolutionary Model ◽  
Online Information ◽  
Information Cascades ◽  
Agent Based Model ◽  
Agent Based ◽  
Highly Active ◽  
Online Social Media

AbstractHuman decision-making is subject to the biological limits of cognition. The fluidity of information propagation over online social media often leads users to experience information overload. This in turn affects which information received by users are processed and gain a response to, imposing constraints on volumes of, and participation in, information cascades. In this study, we investigate properties contributing to the visibility of online social media notifications by highly active users experiencing information overload via cross-platform social influence. We analyze simulations of a coupled agent-based model of information overload and the multi-action cascade model of conversation with evolutionary model discovery. Evolutionary model discovery automates mechanistic inference on agent-based models by enabling random forest importance analysis on genetically programmed agent-based model rules. The mechanisms of information overload have shown to contribute to a multitude of global properties of online information cascades. We investigate nine characteristics of online messages that may contribute to the prioritization of messages for response. Our results indicate that recency had the largest contribution to message visibility, with individuals prioritizing more recent notifications. Global popularity of the conversation originator had the second highest contribution, and reduced message visibility. Messages that presented opportunity for novel user interaction, yet high reciprocity showed to have relatively moderate contribution to message visibility. Finally, insights from the evolutionary model discovery results helped inform response prioritization rules, which improved the robustness and accuracy of the model of information overload.

Simulations of an Agent-Based Model that Consists of a Large Number of Agents Moving Stochastically in a Wide Bounded Domain

2001 ◽  
Author(s):  
Minoru Tabata ◽  
Akira Ide ◽  
Nobuoki Eshima ◽  
Kyushu Takagi ◽  
Yasuhiro Takei ◽  
...  
Keyword(s):  
Bounded Domain ◽  
Agent Based Model ◽  
Agent Based
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