Trends in parameterization, economics and host behaviour in influenza pandemic modelling: a review and reporting protocol.

BACKGROUND: The volume of influenza pandemic modelling studies has increased dramatically in the last decade. Many models incorporate now sophisticated parameterization and validation techniques, economic analyses and the behaviour of individuals. METHODS: We reviewed trends in these aspects in models for influenza pandemic preparedness that aimed to generate policy insights for epidemic management and were published from 2000 to September 2011, i.e. before and after the 2009 pandemic. RESULTS: We find that many influenza pandemics models rely on parameters from previous modelling studies, models are rarely validated using observed data and are seldom applied to low-income countries. Mechanisms for international data sharing would be necessary to facilitate a wider adoption of model validation. The variety of modelling decisions makes it difficult to compare and evaluate models systematically. CONCLUSIONS: We propose a model Characteristics, Construction, Parameterization and Validation aspects protocol (CCPV protocol) to contribute to the systematisation of the reporting of models with an emphasis on the incorporation of economic aspects and host behaviour. Model reporting, as already exists in many other fields of modelling, would increase confidence in model results, and transparency in their assessment and comparison

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Basic concepts for convection parameterization in weather forecast and climate models: COST Action ES0905 final report

The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized

Quality of Information, Survival, and Incentives

This dissertation is devoted to questions on long run survival, the optimal elicitation of private information, and the optimal order of gathering information. In Chapter 2, I consider an infinite horizon risk sharing game in which players have heterogeneous priors about future endowments, and analyze asymptotic behavior of efficient allocation depending on whether the players have commitment power and whether the players are Bayesian or ambiguity averse (Gilboa and Schmeidler (1989)). As in Blume and Easley (2006), I show that if the players are expected utility maximizing Bayesian learners and have commitment power, only survivors are those with the least incorrect beliefs. All other players starve in the long run. In other cases, no player vanishes. When the players are Bayesian and have no commitment power, no player starves in a Pareto efficient subgame perfect equilibrium. When the players are ambiguity averse and have commitment power, they can agree on a stationary allocation, which means that no player vanishes. When the players are ambiguity averse and have no commitment power, for sufficiently large discount factors, a stationary Pareto efficient allocation with commitment is a subgame perfect equilibrium. In Chapter 3, I consider a principal-agent problem in which a principal elicits an agent’s information when the quality of information provided by the agent depends on the agent’s type. We investigate the impact of the agent’s type dependent outside option on the optimal contract. Under restrictive assumptions on the type dependent outside option and the agent’s vii information structure, I show that the principal admits bad types and good types, but reject intermediate types. By further restricting our attention to a smaller class of decision problems, I show the existence of an optimal contract and construct how to design an optimal contract. Finally, I provide an example in which the principal optimally hires bad types to reduce the expected payment to good types. In the example, the principal actually loses if the agent draws a bad type. In Chapter 4, co-authored with Professor Tilman B¨orgers, we study the optimal order of experimentation, considering a class of dynamic decision problems in which two experiments are available and a decision maker incurs costs of experimentation. Given the class of two binary experiments, there is no non-trivial comparison of sequential experiments. The reason why the decision maker runs a less informative experiment first in some circumstances is because the less informative experiment triggers the second experiment less frequently than the more informative experiment does. This idea allows us to come up with another class of two experiments, for which there exists non-trivial comparison of experiments. Given the second class of experiment, informativeness of static decision problems implies informativeness of dynamic decision problems. That is, it is optimal for the decision maker to run a more informative experiment first in every decision problem under study.PHDEconomicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147638/1/kyhan_1.pd

Learning-based perception and control with adaptive stress testing for safe autonomous air mobility

The use of electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient, high-speed, on-demand air transportation within a metropolitan area is a topic of increasing interest, which is expected to bring fundamental changes to the city infrastructures and daily commutes. NASA, Uber, and Airbus have been exploring this exciting concept of Urban Air Mobility (UAM), which has the potential to provide meaningful door-to-door trip time savings compared with automobiles. However, successfully bringing such vehicles and airspace operations to fruition will require introducing orders-of-magnitude more aircraft to a given airspace volume, and the ability to manage many of these eVTOL aircraft safely in a congested urban area presents a challenge unprecedented in air traffic management. Although there are existing solutions for communication technology, onboard computing capability, and sensor technology, the computation guidance algorithm to enable safe, efficient, and scalable flight operations for dense self-organizing air traffic still remains an open question. In order to enable safe and efficient autonomous on-demand free flight operations in this UAM concept, a suite of tools in learning-based perception and control systems with stress testing for safe autonomous air mobility is proposed in this dissertation. First, a key component for the safe autonomous operation of unmanned aircraft is an effective onboard perception system, which will support sense-and-avoid functions. For example, in a package delivery mission, or an emergency landing event, pedestrian detection could help unmanned aircraft with safe landing zone identification. In this dissertation, we developed a deep-learning-based onboard computer vision algorithm on unmanned aircraft for pedestrian detection and tracking. In contrast with existing research with ground-level pedestrian detection, the developed algorithm achieves highly accurate multiple pedestrian detection from a bird-eye view, when both the pedestrians and the aircraft platform are moving. Second, for the aircraft guidance, a message-based decentralized computational guidance algorithm with separation assurance capability for single aircraft case and multiple cooperative aircraft case is designed and analyzed in this dissertation. The algorithm proposed in this work is to formulate this problem as a Markov Decision Process (MDP) and solve it using an online algorithm Monte Carlo Tree Search (MCTS). For the multiple cooperative aircraft case, a novel coordination strategy is introduced by using the logit level-$k$ model in behavioral game theory. To achieve higher scalability, we introduce the airspace sector concept into the UAM environment by dividing the airspace into sectors, so that each aircraft only needs to coordinate with aircraft in the same sector. At each decision step, all of the aircraft will run the proposed computational guidance algorithm onboard, which can guide all the aircraft to their respective destinations while avoiding potential conflicts among them. In addition, to make the proposed algorithm more practical, we also consider the communication constraints and communication loss among the aircraft by modifying our computational guidance algorithms given certain communication constraints (time, bandwidth, and communication loss) and designing air-to-air and air-to-ground communication frameworks to facilitate the computational guidance algorithm. To demonstrate the performance of the proposed computational guidance algorithm, a free-flight airspace simulator that incorporates environment uncertainty is built in an OpenAI Gym environment. Numerical experiment results over several case studies including the roundabout test problem show that the proposed computational guidance algorithm has promising performance even with the high-density air traffic case. Third, to ensure the developed autonomous systems meet the high safety standards of aviation, we propose a novel, simulation driven approach for validation that can automatically discover the failure modes of a decision-making system, and optimize the parameters that configure the system to improve its safety performance. Using simulation, we demonstrate that the proposed validation algorithm is able to discover failure modes in the system that would be challenging for humans to find and fix, and we show how the algorithm can learn from these failure modes to improve the performance of the decision-making system under test

A Model of Persuasion - With Implications for Financial Markets

We propose a model of the phenomenon of persuasion. We argue that individual beliefs evolve in a way that overweights the opinions and information of individuals whom they "listen to" relative to other individuals. Such agents can be understood to be acting as though they believe they listen to a representative sample of the individuals with valuable information, even though they may not. We analyze dynamics and convergence of beliefs, characterizing when agents' beliefs converge over time to the same beliefs, and when they instead diverge. Convergent beliefs can be characterized as the weighted average of agents' initial beliefs, and these weights can be interpreted as a measure of ``influence.'' We then explore implications in an asset trading setting. Here we demonstrate that agents profit from being influential as well as being accurate. When agents' choice of whom to listen to is endogenous, we show that an individual's influence can be persistent, even though the individual may be inaccurate.