Computational Model Library

Displaying 10 of 11 results for "Rodolfo Baggio" clear search

Modeling information Asymmetries in Tourism

Jacopo A. Baggio Rodolfo Baggio | Published Monday, January 09, 2012 | Last modified Saturday, April 27, 2013

A very simple model elaborated to explore what may happens when buyers (travelers) have more information than sellers (tourist destinations)

Landscape connectivity and predator–prey population dynamics

Jacopo A. Baggio | Published Thursday, November 10, 2011 | Last modified Saturday, April 27, 2013

A simple model to assess the effect of connectivity on interacting species (i.e. predator-prey type)

The wisdom of the crowd refers to the phenomenon in which a group of individuals, each making independent decisions, can collectively arrive at highly accurate solutions—often more accurate than any individual within the group. This principle relies heavily on independence: if individual opinions are unbiased and uncorrelated, their errors tend to cancel out when averaged, reducing overall bias. However, in real-world social networks, individuals are often influenced by their neighbors, introducing correlations between decisions. Such social influence can amplify biases, disrupting the benefits of independent voting. This trade-off between independence and interdependence has striking parallels to ensemble learning methods in machine learning. Bagging (bootstrap aggregating) improves classification performance by combining independently trained weak learners, reducing bias. Boosting, on the other hand, explicitly introduces sequential dependence among learners, where each learner focuses on correcting the errors of its predecessors. This process can reinforce biases present in the data even if it reduces variance. Here, we introduce a new meta-algorithm, casting, which captures this biological and computational trade-off. Casting forms partially connected groups (“castes”) of weak learners that are internally linked through boosting, while the castes themselves remain independent and are aggregated using bagging. This creates a continuum between full independence (i.e., bagging) and full dependence (i.e., boosting). This method allows for the testing of model capabilities across values of the hyperparameter which controls connectedness. We specifically investigate classification tasks, but the method can be used for regression tasks as well. Ultimately, casting can provide insights for how real systems contend with classification problems.

This model aims to investigate how different type of learning (social system) and disturbance specific attributes (ecological system) influence adoption of treatment strategies to treat the effects of ecological disturbances.

The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure

Comparing agent-based models on experimental data of irrigation games

Marco Janssen Jacopo A. Baggio | Published Tuesday, July 02, 2013 | Last modified Wednesday, July 03, 2013

Comparing 7 alternative models of human behavior and assess their performance on a high resolution dataset based on individual behavior performance in laboratory experiments.

Effect of communication in irrigation games

Jacopo A. Baggio Marco Janssen | Published Wednesday, January 14, 2015 | Last modified Wednesday, August 09, 2017

The model includes different formulations how agents make decisions in irrigation games and this is compared with empirical data. The aim is to test different theoretical models, especially explaining effect of communication.

Exploring how learning and social-ecological networks influence management choice set and their ability to increase the likelihood of species coexistence (i.e. biodiversity) on a fragmented landscape controlled by different managers.

The model objective’s is to explore the management choice set to uncover which subsets of strategies are most effective at maximizing species coexistence on a fragmented landscape.

We propose an agent-based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the bargaining model by Axtell, Epstein and Young.

Displaying 10 of 11 results for "Rodolfo Baggio" clear search

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