No Arabic abstract
Cardio-pulmonary arrest is a common emergency situation causing over 400,000 deaths per year, more than a 1000 per day, in the USA alone. The goal of this work is to develop an agent based computer simulator that will allow trainers to experiment with different communication protocols, such as those found in air traffic control. This paper describes the first step in designing the simulator development. The design is based on an analysis of communications during real life training simulations using the FIPA standard categories.
It is known that individual opinions on different policy issues often align to a dominant ideological dimension (e.g. left vs. right) and become increasingly polarized. We provide an agent-based model that reproduces these two stylized facts as emergent properties of an opinion dynamics in a multi-dimensional space of continuous opinions. The mechanisms for the change of agents opinions in this multi-dimensional space are derived from cognitive dissonance theory and structural balance theory. We test assumptions from proximity voting and from directional voting regarding their ability to reproduce the expected emerging properties. We further study how the emotional involvement of agents, i.e. their individual resistance to change opinions, impacts the dynamics. We identify two regimes for the global and the individual alignment of opinions. If the affective involvement is high and shows a large variance across agents, this fosters the emergence of a dominant ideological dimension. Agents align their opinions along this dimension in opposite directions, i.e. create a state of polarization.
An agent-based model with interacting low frequency liquidity takers inter-mediated by high-frequency liquidity providers acting collectively as market makers can be used to provide realistic simulated price impact curves. This is possible when agent-based model interactions occur asynchronously via order matching using a matching engine in event time to replace sequential calendar time market clearing. Here the matching engine infrastructure has been modified to provide a continuous feed of order confirmations and updates as message streams in order to conform more closely to live trading environments. The resulting trade and quote message data from the simulations are then aggregated, calibrated and visualised. Various stylised facts are presented along with event visualisations and price impact curves. We argue that additional realism in modelling can be achieved with a small set of agent parameters and simple interaction rules once interactions are reactive, asynchronous and in event time. We argue that the reactive nature of market agents may be a fundamental property of financial markets and when accounted for can allow for parsimonious modelling without recourse to additional sources of noise.
In our multi-agent model agents generate wealth from repeated interactions for which a prisoners dilemma payoff matrix is assumed. Their gains are taxed by a government at a rate $alpha$. The resulting budget is spent to cover administrative costs and to pay a bonus to cooperative agents, which can be identified correctly only with a probability $p$. Agents decide at each time step to choose either cooperation or defection based on different information. In the local scenario, they compare their potential gains from both strategies. In the global scenario, they compare the gains of the cooperative and defective subpopulations. We derive analytical expressions for the critical bonus needed to make cooperation as attractive as defection. We show that for the local scenario the government can establish only a medium level of cooperation, because the critical bonus increases with the level of cooperation. In the global scenario instead full cooperation can be achieved once the cold-start problem is solved, because the critical bonus decreases with the level of cooperation. This allows to lower the tax rate, while maintaining high cooperation.
Good communication is essential within teams dealing with emergency situations. In this paper we look at communications within a resuscitation team performing cardio-pulmonary resuscitation. Communication underpins efficient collaboration, joint coordination of work, and helps to construct a mutual awareness of the situation. Poor communication wastes valuable time and can ultimately lead to life-threatening mistakes. Although training sessions frequently focus on medical knowledge and procedures, soft skills, such as communication receive less attention. This paper analyses communication problems in the case of CPR and proposes an architecture that merges a situation awareness model and the belief-desire-intention (BDI) approach in multi-agent systems. The architecture forms the basis of an agent-based simulator used to assess communication protocols in CPR teams.
The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.