Six 3-min Ignites (IGNITE) Session 1

Abstract: In non-equilibrium statistical physics, a deep understanding of the role played by the entropy production is nowadays an open and fundamental question. In the seminal work of Schnakenberg (Rev. Mod. Phys. (1976), 48(4):571), he introduced an information-based formula for the entropy production of systems described by Master Equation. However, the physical meaning of such a formulation and its link with the thermodynamic entropy production is still debated. To shed some light on this problem, we study a Master Equation system with symmetric transition rates coupled to an environment by the input of an external flux current (i.e. detailed balance does not hold in this case). Interestingly, the problem can be mapped into a classical electrical circuit, and we show analytically and numerically that the Schnakenberg's entropy production is proportional to the heat dissipated by the circuit, and therefore to its classical entropy production. This result provides a strong evidence of the connection between an information-based quantity and thermodynamics. Finally, we can also extend our framework through a perturbative analysis to the case of slightly asymmetric transition rates. Close

Abstract: Soil-transmitted helminthiasis (intestinal worms) is a collection of neglected tropical diseases affecting more than 1 billion people worldwide. These infections cause severe morbidity in school-aged children in whom the majority of worms are harboured. De-worming treatments are available through mass drug administration (MDA); however recent research has concluded that treatment of children alone is not sufficient to break the cycle of transmission in a high transmission setting. We develop a stochastic transmission model focusing on the household structure in the population. Using Markov Chain Monte Carlo (MCMC) and Approximate Bayesian Computation (ABC) methods we fit the model to a cross-sectional study of worm burdens in rural Nigeria and evaluate the effectiveness of alternative MDA strategies that prioritise the treatment of `wormy` households. We discuss how the approach taken enables the utilisation of efficient MCMC methods up to a certain model complexity after which computational considerations necessitate the use of likelihood-free inference methods such as ABC. Close

Abstract: The perspective of social-ecological systems (SESs) postulates that humans and nature domains are linked in complex ways. Changes in one domain often produce changes in the other domain that might bring the SES close to a threshold that, if crossed, changes the whole identity of the SES. The proper use of early warning signals plays a critical role in preventing the crossing of an undesired threshold. For any given SES there are different views of what system’s dynamics is desirable. Thus, decisions about future states must acknowledge those multiple views. In that sense, the development of a boundary object (BO) −an artifact that facilitates communication among stakeholders− provides a means for generating the necessary knowledge about a system’s thresholds taking into consideration the salient issues and concerns, in a way that is legitimate and credible for all stakeholders. Through the case study of SESs delimited by the gray whale breeding coastal lagoons in Baja California, Mexico, we illustrate the development of a BO in the form of a hybrid model. Our hybrid model (a model that combines a dynamic-system model with an agent-based model) aims to produce an early warning signal to be used in the development of regulations for activities related to gray whale watching. Because in essence the identity of the SES is given by the gray whale, we will first elicit a set of scenarios regarding the carrying capacity of whale-watching boats through a system model. Next, we will couple the system model to an agent-based model that incorporates the social-economic domain. The BO thus produced, will be tested in the following year. Close

Abstract: The Louvain algorithm is one of the most widely used and fastest known algorithm for uncovering communities in complex networks. The algorithm consists of a local move heuristic where each node is assigned to its locally optimal community, after which the graph is aggregated and the procedure is reiterated on the aggregated graph. However, the local move heuristic repeatedly considers moving nodes to neighbouring communities, even though their local environment remained unchanged. We here suggest to only consider nodes which can be considered unstable: their local environment changed during the course of the local move heuristic. This fast local move algorithm shows considerably faster runtimes, speeding up the Louvain algorithm over 20 times in experiments, without degrading the quality of the uncovered partition. Close

Abstract: Is Complexity Economics just an extension to standard economics or a radical different way of seing economics? We think that today, 30 years after the first attempts of the Santa Fe Institute to define Complexity Economics (CE) hesitating about these two directions, the answer is now clear. CE provides a totally different framework for economic analysis. The objectif here is to define this framework more precisely. This is indeed a challenge since the first point upon almost everybody agrees is that there is no one definition of what complexity is. We think that the answer to this question depends on the level of generality at which one positions herself. At a paradigmatic level, it is possible to find an encompassing characterization of CE. Following Kuhn, a paradigm is a framework which defines the relevant problems and accepted methods, allowing for a greater efficiency in research: a common language and world apprehension that enhance the diffusion of works and canalizes investigations. We develop such definition around various level: philosophical, ontological, methodological, core concept and agenda levels. We here inform these 5 levels in order to provide a broad definition of the paradigm of CE. Close

Abstract: There are 201 metro systems in the world used by 115 million users daily. A better understanding of rapid transit systems (RTS) can lead to improved performance, safety, and comfort. Using computer simulations, we can test different regulatory methods to compare their performance. An important subject for RTS is the control of train frequency, also known as the headway. If headways are equal then passenger waiting time at stations will be minimized. However, this configuration is unstable. There are three main causes that can deviate headways, two in the interstation segment (trains going faster or slower than expected), and one at stations (changes on passenger demand). The possible solutions demand adaptive strategies. Self-organizing methods are useful to adjust the intervals of waiting time at stations, considering the demand of passengers resulting in a supra-optimal performance in RTS due to a slower is faster effect. If the delay appears on the interstation segment the self-organizing method can correct the headway when the train arrives at station using local information of the environment. In this work we present a realistic agent-based model for RTS that evolves in discrete time and continuous space. We use it to test adaptive strategies to control headways. The model has two main components, an extended version of the Gipps Model (a microscopic car-following model) and a variation of a self-organizing method to regulate headways. To calibrate the variables of the model we consider a real scenario of a Mexico City metro line and recreate a passenger dynamics to using station entry data. We compare the performance between the actual configuration and the self-organizing regulatory methods. Our results show the potential benefit of using self-organization to regulate RTS and its implementation viability for real systems: rapid transit and public transportation in general. Close