Ernesto Carrella
Biographic Data
| ID | 2285855 |
|---|---|
| NAME | Ernesto Carrella |
| GIVEN NAMES | Ernesto |
| FAMILY NAME | Carrella |
| SIGNATURE | CARRELLA E |
| AFFILIATIONS | University of Oxford |
| ORCID | 0000-0002-1045-6787 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
To eat or not to Eat
Food loss and waste are increasingly recognized as the outcomes of complex, interconnected social and environmental dynamics rather than isolated individual choices. In this study, we adopt a complex systems approach to explore how micro-level behaviors and interactions within commercial dining settings give rise to emergent patterns of food waste. We present an agent-based model that integrates a psychologically realistic representation of indiv…
Modelling Adaptive and Anticipatory Human Decision-Making in Complex Human-Environment Systems
To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g. fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within population…
No Free Lunch when Estimating Simulation Parameters
In this paper, we have estimated the parameters of simulation models to find which of estimation algorithms performs better. Unfortunately, no single algorithm was the best for all or even most of the models. Rather, five main results emerge from this research. First, each algorithm was the best estimator for at least one parameter. Second, the best estimation algorithm varied not only between models but even between parameters of the same model.…
Calibrating Agent-Based Models with Linear Regressions
In this paper, we introduce a simple way to parametrize simulation models by using regularized linear regression. Regressions bypass the three major challenges of calibrating by minimization: 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. We present five example estimations: a statistical fit, a biologic…
Simple Adaptive Rules Describe Fishing Behaviour Better than Perfect Rationality in the US West Coast Groundfish Fishery
Analytic Versus Computational Cognitive Models
Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without ap…
A computational approach to managing coupled human–environmental systems
Sustainable management of complex human–environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed b…
Zero-Knowledge Traders
We provide simple general-purpose rules for agents to buy inputs, sell outputs and set production rates. The agents proceed by trial and error using PID controllers to adapt to past mistakes. These rules are computationally inexpensive, use little memory and have zero-knowledge of the outside world. We place these zero-knowledge agents in a monopolist and a competitive market where they achieve outcomes similar to what standard economic theory pr…
Analytic Versus Computational Cognitive Models
Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without ap…
A computational approach to managing coupled human–environmental systems
Sustainable management of complex human–environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed b…
Zero-Knowledge Traders
We provide simple general-purpose rules for agents to buy inputs, sell outputs and set production rates. The agents proceed by trial and error using PID controllers to adapt to past mistakes. These rules are computationally inexpensive, use little memory and have zero-knowledge of the outside world. We place these zero-knowledge agents in a monopolist and a competitive market where they achieve outcomes similar to what standard economic theory pr…
A computational approach to managing coupled human–environmental systems
Sustainable management of complex human–environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed b…
Analytic Versus Computational Cognitive Models
Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without ap…
Calibrating Agent-Based Models with Linear Regressions
In this paper, we introduce a simple way to parametrize simulation models by using regularized linear regression. Regressions bypass the three major challenges of calibrating by minimization: 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. We present five example estimations: a statistical fit, a biologic…
Simple Adaptive Rules Describe Fishing Behaviour Better than Perfect Rationality in the US West Coast Groundfish Fishery
No Free Lunch when Estimating Simulation Parameters
In this paper, we have estimated the parameters of simulation models to find which of estimation algorithms performs better. Unfortunately, no single algorithm was the best for all or even most of the models. Rather, five main results emerge from this research. First, each algorithm was the best estimator for at least one parameter. Second, the best estimation algorithm varied not only between models but even between parameters of the same model.…
Modelling Adaptive and Anticipatory Human Decision-Making in Complex Human-Environment Systems
To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g. fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within population…
To eat or not to Eat
Food loss and waste are increasingly recognized as the outcomes of complex, interconnected social and environmental dynamics rather than isolated individual choices. In this study, we adopt a complex systems approach to explore how micro-level behaviors and interactions within commercial dining settings give rise to emergent patterns of food waste. We present an agent-based model that integrates a psychologically realistic representation of indiv…
Computer Science (6 works) · Artificial Intelligence (4 works) · Economics (3 works) · Algorithm (2 works) · Auction Theory and Applications (2 works) · Complex Systems and Time Series Analysis (2 works) · Econometrics (2 works) · Fisheries management (2 works) · Fishing (2 works) · Mathematical optimization (2 works)