Agent-based simulation of power exchange with heterogeneous production companies

Since early nineties, worldwide production and distribution of electricity has been characterized by a progressive liberalization. The state-owned monopolistic production of electricity has been substituted by organized power exchanges (PEs). PEs are markets which aggregate the effective supply and demand of electricity. Usually spot-price market are Day Ahead Market (DAM) and are requested in order to provide an indication for the hourly unit commitment. This first session of the complex daily energy market collects and orders all the offers, determining the market price by matching the cumulative demand and supply curves for every hour of the day after according to a merit order rule. Subsequent market sessions (also online) operate in order to guarantee the feasibility and the security of this plan. The electric market is usually characterized by a reduced number of competitors, thus oligopolistic scenario may arise. Understanding how electricity prices depend on oligopolistic behavior of suppliers and on production costs has become a very important issue. Several restructuring designs for the electric power industry have been proposed. Main goal is to increase the overall market efficiency, trying to study, to develop and to apply different market mechanisms. Auction design is the standard domain for commodity markets. However, properties of different auction mechanism must be studied and determined correctly before their appliance. Generally speaking, different approaches have been proposed in the literature. Game theory analysis has provided an extremely useful methodology to study and derive properties of economic "games", such as auctions. Within this context, an interesting computational approach, for studying market inefficiencies, is the theory of learning in games. This methodology is useful in the context of infinitely repeated games. This paper investigates the nature of the clearing mechanism comparing two different methods, i.e., discriminatory and uniform auctions. The theoretical framework used to perform the analysis is the theory of learning in games. We consider an inelastic demand faced by sellers which use learning algorithms to understand proper strategies for increasing their profits. We model the auction mechanism in two different duopolistic scenario, i.e., a low demand situation, where one seller can clear all the demand, and a high demand condition, where both sellers are requested. Moreover, heterogeneity in the linear cost function is considered. Consistent results are achieved with two different learning algorithmsAgent-based simulation; power-exchange market; market power, reinforcement learning, electricity production costs

Macroprudential policies in an agent-based artificial economy

Basel III is a recently-agreed regulatory standard for bank capital adequacy with focus on the macroprudential dimension of banking regulation, i.e., the system-wide implications of banks' lending and risk. An important Basel III provision is to reduce procyclicality of present banking regulation and promote countercyclical capital buffers for banks. The Eurace agent-based macroeconomic model and simulator has been recently showed to be able to reproduce a credit-fueled boom-bust dynamics where excessive bank leverages, while benefitting in the short term, have destabilizing effects in the medium-long. In this paper. we employ the Eurace model to test regulatory policies providing time varying capital requirements for banks, based on mechanisms that enforce banks to build up or release capital buffers, according to the overall conditions of the economy. As conditioning variables for these dynamic policies, both the unemployment rate and the aggregate credit growth have been considered. Results show that the dynamic regulation of capital requirements is generally more successful than fixed tight capital requirements in stabilizing the economy and improving the macroeconomic performance.Basel III, macroprudential regulation, agent-based models and simulation

Spatial filters selection towards a rehabilitation BCI

Introducing BCI technology in supporting motor imagery (MI) training has revealed the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in stroke patients. To provide the most accurate and personalized feedback during the treatment, several stages of the electroencephalographic signal processing have to be optimized, including spatial filtering. This study focuses on data-independent approaches to optimize spatial filtering step. Specific aims were: i) assessment of spatial filters' performance in relation to the hand and foot scalp areas; ii) evaluation of simultaneous use of multiple spatial filters; iii) minimization of the number of electrodes needed for training. Our findings indicate that different spatial filters showed different performance related to the scalp areas considered. The simultaneous use of EEG signals conditioned with different spatial filters could either improve classification performance or, at same level of performance could lead to a reduction of the number of electrodes needed for successive training, thus improving usability of BCIs in clinical rehabilitation context

The Promotoer: a successful story of translational research in BCI for motor rehabilitation

Several groups have recently demonstrated in the context of randomized controlled trials (RCTs) how sensorimotor Brain-Computer Interface (BCI) systems can be beneficial for post-stroke motor recovery. Following a successful RCT, at Fondazione Santa Lucia (FSL) a further translational effort was made with the implementation of the Promotœr, an all in-one BCIsupported MI training station. Up to now, 25 patients underwent training with the Promotɶr during their admission for rehabilitation purposes (in add-on to standard therapy). Two illustrative cases are presented. Though currently limited to FSL, the Promotɶr represents a successful story of translational research in BCI for stroke rehabilitation. Results are promising both in terms of feasibility of a BCI training in the context of a real rehabilitation program and in terms of clinical and neurophysiological benefits observed in the patients

The impact of banks’ capital adequacy regulation on the economic system: an agent-based approach

Since the start of the financial crisis in 2007, the debate on the proper level leverage of financial institutions has been flourishing. The paper addresses such crucial issue within the Eurace arti?cial economy, by considering the effects that different choices of capital adequacy ratios for banks have on main economic indicators. The study also gives us the opportunity to examine the outcomes of the Eurace model so to discuss the nature of endogenous money, giving a contribution to a debate that has grown stronger over the last two decades. A set of 40 years long simulations have been performed and examined in the short (first 5 years), medium (the following 15 years) and long (the last 20 years) run. Results point out a non-trivial dependence of real economic variables such as the Gross Domestic Product (GDP), the unemployment rate and the aggregate capital stock on banks’ capital adequacy ratios; this dependence is in place due to the credit channel and varies significantly according to the chosen evaluation horizon. In general, while boosting the economy in the short run, regulations allowing for a high leverage of the banking system tend to be depressing in the medium and long run. Results also point out that the stock of money is driven by the demand for loans, therefore supporting the theory of endogenous nature of credit money.Agent-based models, banking regulation

Noise suppression using optimum filtering of OCs generated by a multiport encoder/decoder

We propose a novel receiver configuration using an extreme narrow band-optical band pass filter (ENB-OBPF) to reduce the multiple access interference (MAI) and beat noises in an optical code division multiplexing (OCDM) transmission. We numerically and experimentally demonstrate an enhancement of the code detectability, that allows us to increase the number of users in a passive optical network (PON) from 4 to 8 without any forward error correction (FEC)

GUIDER: a GUI for semiautomatic, physiologically driven EEG feature selection for a rehabilitation BCI

GUIDER is a graphical user interface developed in MATLAB software environment to identify electroencephalography (EEG)-based brain computer interface (BCI) control features for a rehabilitation application (i.e. post-stroke motor imagery training). In this context, GUIDER aims to combine physiological and machine learning approaches. Indeed, GUIDER allows therapists to set parameters and constraints according to the rehabilitation principles (e.g. affected hemisphere, sensorimotor relevant frequencies) and foresees an automatic method to select the features among the defined subset. As a proof of concept, we compared offline performances between manual, just based on operator’s expertise and experience, and GUIDER semiautomatic features selection on BCI data collected from stroke patients during BCI-supported motor imagery training. Preliminary results suggest that this semiautomatic approach could be successfully applied to support the human selection reducing operator dependent variability in view of future multi-centric clinical trials

EEG Resting-State Brain Topological Reorganization as a Function of Age

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

BCI-assisted training for upper limb motor rehabilitation: estimation of effects on individual brain connectivity and motor functions

The aim of the study is to quantify individual changes in scalp connectivity patterns associated to the affected hand movement in stroke patients after a 1-month training based on BCIsupported motor imagery to improve upper limb motor recovery. To perform the statistical evaluation between pre- and post-training conditions at the single subject level, a resampling approach was applied to EEG datasets acquired from 12 stroke patients during the execution of a motor task with the stroke affected hand before and after the rehabilitative intervention. Significant patterns of the network reinforced after the training were extracted and a significant correlation was found between indices related to the reinforced pattern and the clinical outcome indicated by clinical scales