9 results on '"Dercole, Fabio"'
Search Results
2. A deterministic eco-genetic model for the short-term evolution of exploited fish stocks.
- Author
-
Dercole, Fabio and Della Rossa, Fabio
- Subjects
- *
FISH stocking , *ECOLOGICAL models , *FISH population genetics , *FISH ecology , *TEMPERATURE effect - Abstract
Eco-genetic models describe the contemporary evolution of quantitative genetic traits by integrating the key ecological aspects with the modes of inheritance. Because of the flexibility in incorporating physiological, ecological, and genetic detail, eco-genetic models are typically individual-based and stochastic. Here we propose a deterministic eco-genetic population model to study the evolution of maturation schedules in exploited fish stocks. It is the first deterministic model addressing the evolution of the probabilistic maturation reaction norm (PMRN)—the genetic trait recognized to control maturation in fish species. The PMRN is the probability of interseasonal maturation as a function of age and size of juvenile individuals. Being independent of the growth trajectory followed by the individual, it can be considered a non-plastic phenotype, under limited change in temperature and climate. We consider the continuous age and size structures of the fish stock and explicitly represent foraging on a single food resource and mating under 1:1 sex ratio. Inheritance follows a single-locus-two-alleles Mendelian rule to limit the number of genotypes. We adapt the standard escalator boxcar train method for the simulation of physiologically structured population models to the case of two-dimensional age distributions—mature individuals being also distributed according to the age at maturation. The model simulations qualitatively reproduce empirical trends, specifically the genotypic redistribution toward early-maturing types under increasing exploitation and the eventual collapse of the stock. Interestingly, increased effort targeted at large sizes seems to erode the genetic diversity, an effect missed by stochastic models that might serve as warning to fishery managers and policy makers. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
3. Dynamics of economic unions: An agent-based model to investigate the economic and social drivers of withdrawals.
- Author
-
Gracia-Lázaro, Carlos, Dercole, Fabio, and Moreno, Yamir
- Subjects
- *
INTERNATIONAL economic integration , *ECONOMIC models , *BUSINESS conditions , *INCOME inequality , *LOCAL taxation , *SPEED - Abstract
Economic unions are international agreements oriented to increase economic efficiency and establishing political and cultural ties between the member countries. Becoming a member of an existing union usually requires the approval of both the candidate and members, while leaving it may require only the unilateral will of the exiting country. There are many examples of accession of states to previously consolidated economic unions, and a recent example of leaving is the withdrawal of the United Kingdom from the European Union. Motivated by the Brexit process, in this paper we propose an agent-based model to study the determinant factors driving withdrawals from an economic union. We show that both Union and local taxes promote the exits, whereas customs fees out of the Union boost cohesion. Furthermore, heterogeneity in both business conditions and wealth distribution promotes withdrawals, while countries' size diversity does not have a significant effect on them. We also deep into the individual causes that lead to dissatisfaction and, ultimately, to exits. We found that, for low Union taxes, the wealth inequality within the country is the leading cause of anti-Union opinion spreading. Conversely, for high Union taxes, the country's performance turns out to be the main driving force, resulting in a risk of wealthier countries leaving the Union. These findings will be helpful for the design of economic policies and effective informative campaigns. • Agent-based modeling is helpful to study opinion dynamics in economic unions. • Taxes promote economic unions withdrawals, whereas customs fees boost cohesion. • Heterogeneity in both business conditions and wealth distribution promotes exits. • For low taxes, within-country wealth inequality fosters anti-Union opinion spreading. • High Union taxes involves a risk of wealthier countries leaving the Union. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Technological change and fisheries sustainability: The point of view of Adaptive Dynamics
- Author
-
Dercole, Fabio, Prieu, Charlotte, and Rinaldi, Sergio
- Subjects
- *
FISHERIES & the environment , *SUSTAINABILITY , *TECHNOLOGICAL innovations & economics , *OVERFISHING , *TECHNOLOGY & society , *MODEL-based reasoning , *FISH populations - Abstract
The analysis of a simple model shows that exploitation of fish stocks can entrain in the long run the substantial decline or even the collapse of the stock, as well as difficulties in stock recovery, loss of fishery resilience, and reduction of the mean fish size. The results are in agreement with numerous observations, even though they are obtained with a simple model in which the harvesting fleet and the fish stock are considered as unstructured predator and prey. The study is carried out for the typical case of fleet dimension not too sensitive to the year-to-year fluctuations of the stock and assuming that the sole cause of evolution is technological innovation. The analysis is performed by means of Adaptive Dynamics, an approach born in theoretical biology which is used here in the context of technological change. Although the results are qualitatively consistent with those obtained long ago through the principles of bioeconomics, it is fair to stress that the underlying assumptions are different. In fact, in the bioeconomic approach fleet technology does not evolve and fishing effort varies to produce economic optimization, while in the Adaptive Dynamics approach technological innovation is the key driver. The paper is purely theoretical and the proposed model can hardly be tuned on any real fishery. No practical guidelines for managers can therefore be drawn, if not the general conclusion that long-term sustainability of exploited fish stocks can only be achieved if strategic parameters influencing technological change are kept under strict control. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
5. Detection and continuation of a border collision bifurcation in a forest fire model
- Author
-
Dercole, Fabio and Maggi, Stefano
- Subjects
- *
FOREST fires , *FORESTS & forestry , *NATURAL disasters , *DEFORESTATION - Abstract
Abstract: The behavior of the simplest forest fire model is studied in this paper through bifurcation analysis. The model is a second-order continuous-time impact model where vegetational growth is described as a continuous and slow dynamic process, while fires are modeled as instantaneous and disruptive events. The transition from Mediterranean forests (characterized by wild chaotic fire regimes) to savannas and boreal forests (where fires are almost periodic) is recognized to be a catastrophic transition known as border collision bifurcation in the context of discrete-tine systems. In the present case such a bifurcation can be easily detected numerically and then continued by solving a standard boundary-value problem. The result of the analysis complements previous simulation studies and are consistent with biological intuition. [Copyright &y& Elsevier]
- Published
- 2005
- Full Text
- View/download PDF
6. Remarks on the computation of the horizon of a digital terrain
- Author
-
Dercole, Fabio
- Subjects
- *
RELIEF models , *ALGORITHMS , *TRIANGLES , *MODELS & modelmaking - Abstract
The problem of determining the horizon of a terrain viewed from a given point is addressed. A triangulated surface model of the terrain is often used to this aim, because such a model is simple and flexible. In all contributions where the problem is explicitly discussed, it is assumed that the horizon can be determined by computing the upper envelope of a set of segments in the plane. This conjecture is shown here to be wrong. One of the major consequences of this error concerns the determination of the visible portion of a terrain. Indeed, the algorithms available for this purpose process the terrain triangles in distance order from the viewpoint and are based on the updating of the horizon associated with the already visited triangles, that can cast shadow on the not yet visited ones. The consequence of a wrong computation of the horizon is that some invisible portions of the terrain can be classified as visible. Moreover, the extension of the wrongly classified area depends upon the configuration of the terrain and can be arbitrarily large even for an arbitrarily small error in horizon computation. [Copyright &y& Elsevier]
- Published
- 2003
- Full Text
- View/download PDF
7. Forecasting of noisy chaotic systems with deep neural networks.
- Author
-
Sangiorgio, Matteo, Dercole, Fabio, and Guariso, Giorgio
- Subjects
- *
RECURRENT neural networks , *STRUCTURAL dynamics , *FORECASTING , *DETERMINISTIC processes , *TIME series analysis - Abstract
• Feed-forward and LSTM networks are compared in the forecasting of noisy chaotic time series. • Observation noise strongly affects the performance of all the neural predictors. • Recurrent architectures are more suitable for learning the non-stationary dynamics caused by structural noise. • LSTM trained without teacher forcing shows the greatest ability of mitigating the different sources of noise. Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex oscillatory time series on a multi-step horizon. Researchers in the field investigated different machine learning techniques and training approaches on dynamical systems with different degrees of complexity. Still, these analyses are usually limited to noise-free chaotic time series. This paper extends the analysis from a deterministic to a noisy environment, by considering both observation and structural noise. Observation noise is evaluated by adding different levels of artificially-generated random values on deterministic processes obtained from the simulation of four archetypal chaotic systems. A case of structural noise is implemented through a time-varying version of the logistic map, which exhibits a slow structural change of the system's dynamic that makes the system non-stationary. Finally, a time series of ozone concentration in Northern Italy is considered to test the theoretical findings on a real-world case study in which both forms of noise play a significant role. Recurrent neural networks formed by LSTM cells are compared with two benchmark feed-forward architectures. LSTM trained without the standard teacher forcing approach, i.e., with training that replicates the setting used in inference mode, proved to have the best performance in compensating the stochasticity generated by the observation noise and reproducing the structural non-stationarity of the process. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
8. Robustness of LSTM neural networks for multi-step forecasting of chaotic time series.
- Author
-
Sangiorgio, Matteo and Dercole, Fabio
- Subjects
- *
TIME series analysis , *FORECASTING , *NATURAL language processing , *TEACHER training , *RECURRENT neural networks - Abstract
• LSTM nets outperform feed-forward competitors in forecasting noise-free chaotic time series. • Training without teacher forcing (no-TF) enhances the forecasting accuracy and the robustness of the LSTM predictors. • The robustness to the number of data samples provided as input (lags) is especially relevant for real-world applications. • LSTM-no-TF also ensure a uniform predictive power within the chaotic attractor. • LSTM-no-TF nets well predict chaotic dynamics up to 7 Lyapunov times. Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as basic blocks to build sequence to sequence architectures, which represent the state-of-the-art approach in many sequential tasks related to natural language processing. In this work, these architectures are proposed as general purposes, multi-step predictors for nonlinear time series. We analyze artificial, noise-free data generated by chaotic oscillators and compare LSTM nets with the benchmarks set by feed-forward, one-step-recursive and multi-output predictors. We focus on two different training methods for LSTM nets. The traditional one makes use of the so-called teacher forcing, i.e. the ground truth data are used as input for each time step ahead, rather than the outputs predicted for the previous steps. Conversely, the second feeds the previous predictions back into the recurrent neurons, as it happens when the network is used in forecasting. LSTM predictors robustly show the strengths of the two benchmark competitors, i.e., the good short-term performance of one-step-recursive predictors and greatly improved mid-long-term predictions with respect to feed-forward, multi-output predictors. Training LSTM predictors without teacher forcing is recommended to improve accuracy and robustness, and ensures a more uniform distribution of the predictive power within the chaotic attractor. We also show that LSTM architectures maintain good performances when the number of time lags included in the input differs from the actual embedding dimension of the dataset, a feature that is very important when working on real data. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
9. Does the "uptick rule" stabilize the stock market? Insights from adaptive rational equilibrium dynamics.
- Author
-
Dercole, Fabio and Radi, Davide
- Subjects
- *
STOCK exchanges , *PRICE regulation , *PRICE fluctuations , *EQUILIBRIUM - Abstract
• An ARED (adaptive rational equilibrium dynamics) asset pricing model where agents trade according to heterogeneous and adaptive beliefs on the future prices. • The effects of the "uptick rule". • The investigation underlines that the uptick rule meets its intended objective. • The analysis suggests also possible and unwanted side effects of the regulation, such as an excessive swelling of speculative bubbles. This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as Exchange Act Rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were first shown to be responsible for endogenous price fluctuations. The dynamics of stock prices generated by the model, with and without the uptick-rule restriction, are analyzed by pairing the classical fundamental prediction with beliefs based on both linear and nonlinear forecasting rules deriving from the technical analysis of the financial markets. The comparison shows a reduction of downward price movements of undervalued shares when the short selling restriction is imposed. This gives evidence that the uptick rule meets its intended objective. However, the effects of the short selling regulation fade when the intensity of choice to switch trading strategies is high. In addition, the analysis suggests possible side effects of the regulation on price dynamics, such as an excessive swelling of speculative bubbles. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.