A Framework for the Calibration of Social Simulation Models

Ciampaglia, Giovanni Luca

doi:10.1142/s0219525913500306

Cited by 5 publications

(3 citation statements)

References 63 publications

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“…This has become a common challenge, especially with the rise of agent-based modeling in the social sciences [87]. There is a vast literature devoted to developing rigorous methods to test simulation models on empirical data of social phenomena [88,89]. Although no single universal recipe exists, we adopt the common approach of generating synthetic data from our agent-based model and comparing them to the empirical data under appropriate distance measures.…”

Section: Model Evaluationmentioning

confidence: 99%

Social influence and unfollowing accelerate the emergence of echo chambers

et al. 2020

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While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.

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Section: Model Evaluationmentioning

confidence: 99%

Social influence and unfollowing accelerate the emergence of echo chambers

et al. 2020

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“…Kennedy and O'Hagan (2001) introduced the method(see O'Hagan 2006 for a review). Ciampaglia (2013), Parry et al (2013) and Salle and Yıldızoğlu (2014) A variant of this approach interleaves running simulations and training the meta-model to sample more promising areas and achieve better minima. The general framework is the "optimization by model fitting"(see chapter 9 of Sean 2010 for a review; see Michalski 2000 for an early example).…”

Section: Literature Reviewmentioning

confidence: 99%

Indirect inference through prediction

Carrella,

Bailey,

Madsen

2018

Preprint

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By recasting indirect inference estimation as a prediction rather than a minimization and by using regularized regressions, we can bypass the three major problems of estimation: selecting the summary statistics, defining the distance function and minimizing it numerically. By substituting regression with classification we can extend this approach to model selection as well. We present three examples: a statistical fit, the parametrization of a simple real business cycle model and heuristics selection in a fishery agent-based model. The outcome is a method that automatically chooses summary statistics, weighs them and use them to parametrize models without running any direct minimization.

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“…Finally, carefully paying attention to the calibration of the model on actual data (Ciampaglia, 2013). Figure 6.9: in the first row (ir, interest rate) is the raw data with lagged correlations among data, and the same, but as partial correlations; in the second (ird, interest rate differences) row the data are replaced by first differences with lag 1, with their lagged correlations and their lagged partial correlations…”

Section: Possible Developmentsmentioning

confidence: 99%