Agent-based Models of the Economy

Boero, Riccardo; Morini, Matteo; Sonnessa, Michele; Terna, Pietro

doi:10.1057/9781137339812

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…Globally, the approaches followed are different, but the results seem to be in line with the different automotive scenarios of the two countries. Seeking a comparison with an Italian scenario, in [25] the electricity demand forecast for BEVs plus PEVs in 2050 is 35 TWh (with a ratio of 2 to 1 for BEVs), while the electricity demand estimate obtained in this work for the same year is 27.2 TWh for BEVs alone. The results are very close because electricity consumption does not scale linearly between BEVs and PEVs.…”

mentioning

confidence: 80%

Concerns on Full Electric Mobility and Future Electricity Demand in Italy

Falfari

Bianchi

2023

Energies

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This work aims to provide an energy and critical analysis of the new “ecological scenario” that sees the world shift the transport sector from the classic fossil-fueled traction towards a purely electric traction. Is it a transition to a cleaner and more environmentally friendly world? This is the central question we need to answer. In this context of forced electrification of passenger vehicles, the authors estimated the electricity surplus needed in Italy to face this transition with a projection up to 2050, considering the electricity demand of purely electric vehicles and for the production of green hydrogen for the fuel supply of fuel cell vehicles. Throughout this investigation, the authors discovered that, in 2050, the surplus of electricity to be produced compared to the current production in Italy (year 2021) is equal to +27.6% (1). This value increases if two limit scenarios are considered: it becomes +40.0% within a limit scenario in which the entire private car fleet is made up of pure electric traction vehicles only (2). It becomes +100.6% in the hypothesis that the entire fleet of passenger cars is made up of fuel cell vehicles powered exclusively by green hydrogen (3).

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confidence: 80%

Concerns on Full Electric Mobility and Future Electricity Demand in Italy

Falfari

Bianchi

2023

Energies

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“…Traditionally, market dynamics are studied as phenomena in themselves with a top-down approach to complexity inference, for example by using statistical or econometric models [1]. Yet trading in real financial markets comes as a result of the collective interactions of human actors, either directly in the form of economic traders, or indirectly in the form of investors imperatives that constrain algorithmic trading strategies [2]. Understanding how these two trading approaches and their gap differ can potentially be bridged using multi-agent systems (MAS), or agent-based models (ABM), which have been sought after by industry practitioners and regulators alike [3].…”

Section: Introductionmentioning

confidence: 99%

“…Understanding how these two trading approaches and their gap differ can potentially be bridged using multi-agent systems (MAS), or agent-based models (ABM), which have been sought after by industry practitioners and regulators alike [3]. They are currently an active area of research [4,5] and tools to study cross-market structure [6], market regulatory impact [2], the law of supply and demand [7], high-frequency trading [8,9], quantitative easing [10], and other exogenous effects [11]. Modern MAS applied to financial markets have been able to reenact the so-called market stylised facts [5], which are statistical properties of the stock price return and volatility signals, observed across different markets, assets and time scales.…”

Section: Introductionmentioning

confidence: 99%

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study

Lussange,

Vrizzi,

Palminteri

et al. 2024

PLoS ONE

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Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

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“…This surge in electricity usage has particularly affected the industrial and domestic sectors. Notably, the domestic sector witnessed a spike in electricity consumption following the pandemic, driven by altered habits due to increased remote work practices ("smart working") and stay-at-home mandates [1], [2]. This trend has persisted and intensified, partly influenced by the growing adoption of electricity for final consumption [1].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic cooling techniques’ effect on the average monthly performance

Rollo,

Ferraro,

Bevilacqua

2024

Acta IMEKO

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Nowadays, mitigating climate-altering emissions resulting from air conditioning and mechanical ventilation of indoor spaces is of utmost importance. Encourage the adoption of renewable energy sources for power generation is a critical approach in this regard. Among the available technologies, photovoltaic technology stands as the most mature option. However, it does have limitations, such as reduced efficiency and performance degradation at elevated temperatures. To enhance the efficiency of photovoltaic systems, various solutions have been proposed over time, with significant research focusing on the exploration of new materials. One of the most promising solutions involves panel cooling through the utilization of external fluids, either in a forced or natural manner. Furthermore, the extracted heat from this cooling process can be effectively reused in other industrial processes, adding to its appeal. Nonetheless, despite its potential, the application of panel cooling technology is relatively recent, and assessing its suitability in specific scenarios at an early stage can be challenging. Currently, there is a lack of clear and straightforward methodologies to evaluate the performance gains achievable through the implementation of panel cooling. The primary objective of this research is to present an innovative methodology that can effectively assess panel cooling efficiency on an average daily-monthly basis. Specifically, we propose corrective parameters that modify the widely used Siegel method, which determines the monthly average daily efficiency of uncooled panels. Throughout the study, it has become evident that the input values derived from the UNI standard do not fully represent the real-world conditions. This finding may indicate the necessity for regulatory updates to accurately account for the practical operational environment.

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Agent-based Models of the Economy

Cited by 13 publications

References 30 publications

Concerns on Full Electric Mobility and Future Electricity Demand in Italy

Concerns on Full Electric Mobility and Future Electricity Demand in Italy

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study

Photovoltaic cooling techniques’ effect on the average monthly performance

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