Parameter-free aggregation of value functions from multiple experts and uncertainty assessment in multi-criteria evaluation

This paper makes a threefold contribution to spatial multi-criteria evaluation (MCE): firstly by presenting a new method concerning value functions, secondly by comparing different approaches to assess the uncertainty of a MCE outcome, and thirdly by presenting a case-study on land-use change. Even though MCE is a well-known methodology in GIScience, there is a lack of practicable approaches to incorporate the potentially diverse views of multiple experts in defining and standardizing the values used to implement input criteria. We propose a new method that allows generating and aggregating non-monotonic value functions, integrating the views of multiple experts. The new approach only requires the experts to provide up to four values, making it easy to be included in questionnaires. We applied the proposed method in a case study that uses MCE to assess the potential of future loss of vineyards in a wine-growing area in Switzerland, involving 13 experts from research, consultancy, government, and practice. To assess the uncertainty of the outcome three different approaches were used: firstly, a complete Monte Carlo simulation with the bootstrapped inputs, secondly a one-factor-at-a-time variation, and thirdly bootstrapping of the 13 inputs with subsequent analytical error propagation. The complete Monte Carlo simulation has shown the most detailed distribution of the uncertainty. However, all three methods indicate a general trend of areas with lower likelihood of future cultivation to show a higher degree of relative uncertainty

Parameter-free aggregation of value functions from multiple experts and uncertainty assessment in multi-criteria evaluation

This paper makes a threefold contribution to spatial multi-criteria evaluation (MCE): firstly by presenting a new method concerning value functions, secondly by comparing different approaches to assess the uncertainty of a MCE outcome, and thirdly by presenting a case-study on land-use change. Even though MCE is a well-known methodology in GIScience, there is a lack of practicable approaches to incorporate the potentially diverse views of multiple experts in defining and standardizing the values used to implement input criteria. We propose a new method that allows generating and aggregating non-monotonic value functions, integrating the views of multiple experts. The new approach only requires the experts to provide up to four values, making it easy to be included in questionnaires. We applied the proposed method in a case study that uses MCE to assess the potential of future loss of vineyards in a wine-growing area in Switzerland, involving 13 experts from research, consultancy, government, and practice. To assess the uncertainty of the outcome three different approaches were used: firstly, a complete Monte Carlo simulation with the bootstrapped inputs, secondly a one-factor-at-a-time variation, and thirdly bootstrapping of the 13 inputs with subsequent analytical error propagation. The complete Monte Carlo simulation has shown the most detailed distribution of the uncertainty. However, all three methods indicate a general trend of areas with lower likelihood of future cultivation to show a higher degree of relative uncertainty

Preference Disaggregation: Towards an Integrated Framework

La desagregació de preferències pretén capturar models de preferències mitjançant la descomposició de la informació obtinguda a partir de preferències indirectes que estan en forma d'eleccions holístiques o judicis. Des d'una perspectiva d'ajuda a la presa de decisions multi criteri, aquesta informació es pren com a punt de partida en un procés d'inferència que condueix a model de preferències basat en punts de vista, generalment conflictius, que conjuntament formen una base per a la decisió. L'estudi de les decisions humanes ha rebut una atenció creixent en els últims anys des de diverses disciplines, que inclouen des de les ciències del comportament (anàlisi de decisions, desagregació de preferències), la intel·ligència artificial (aprenentatge de preferències), fins a l'economia i el màrqueting (teoria de l'elecció). Les tres corrents, encara que originades per diferents filosofies, convergeixen ràpidament cap a una comprensió integral de les preferències, que és l'element bàsic per a les decisions i accions humanes. Aquesta tesi doctoral aprofundeix en aquesta àrea de recerca mitjançant la introducció d'un marc analític integrat que permet capturar les preferències d'una forma complexa a partir de l'observació d'opcions holístiques, decisions i judicis.La desagregación de preferencias pretende capturar modelos de preferencias mediante la descomposición de la información obtenida con preferencias indirectas que están en forma de elecciones holísticas o juicios. Desde una perspectiva de ayuda a la toma de decisiones multicriterio, dicha información se toma como punto de partida en un proceso de inferencia que conduce a modelo de preferencias basado en puntos de vista, generalmente conflictivos, que conjuntamente forman una base para la decisión. El estudio de las decisiones humanas ha recibido una atención creciente en los últimos años desde varias disciplinas, que incluyen desde las ciencias del comportamiento (análisis de decisiones, desagregación de preferencias), la inteligencia artificial (aprendizaje de preferencias), hasta la economía y el márqueting (teoría de la elección). Las tres corrientes, aunque originadas por diferentes filosofías, convergen rápidamente hacia una comprensión integral de las preferencias, que es el elemento básico para las decisiones y acciones humanas. Esta tesis doctoral profundiza en esta área de investigación mediante la introducción de un marco analítico integrado que permite capturar las preferencias de una forma compleja a partir de la observación de opciones holísticas, decisiones y juicios.Preference disaggregation aims at capturing preference models by decomposing indirect preference information that are in form of holistic choices or judgments. From a multiple criteria decision aiding perspective, such information is taken as input to an inference procedure that yields to a preference model based on all the, usually conflicting, points of view that together form a basis for the judgments. Studying human judgments and choices has received increasing attention in the last few years from several disciplines, including behavioral science (decision analysis, preference disaggregation), artificial intelligence (preference learning), and economics and marketing (choice modeling). The three streams, although originated from different philosophies, are converging rapidly to a comprehensive understanding of human preferences, that is the main element of decisions and actions. This doctoral dissertation sheds light on this phenomenon by introducing an integrated analytical framework that allows capturing preferences of a complex form by observing holistic choices, decisions, and judgments

FACMAC: Factored Multi-Agent Centralised Policy Gradients

We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces. Like MADDPG, a popular multi-agent actor-critic method, our approach uses deep deterministic policy gradients to learn policies. However, FACMAC learns a centralised but factored critic, which combines per-agent utilities into the joint action-value function via a non-linear monotonic function, as in QMIX, a popular multi-agent Q-learning algorithm. However, unlike QMIX, there are no inherent constraints on factoring the critic. We thus also employ a nonmonotonic factorisation and empirically demonstrate that its increased representational capacity allows it to solve some tasks that cannot be solved with monolithic, or monotonically factored critics. In addition, FACMAC uses a centralised policy gradient estimator that optimises over the entire joint action space, rather than optimising over each agent's action space separately as in MADDPG. This allows for more coordinated policy changes and fully reaps the benefits of a centralised critic. We evaluate FACMAC on variants of the multi-agent particle environments, a novel multi-agent MuJoCo benchmark, and a challenging set of StarCraft II micromanagement tasks. Empirical results demonstrate FACMAC's superior performance over MADDPG and other baselines on all three domains

The estimation of reward and value in reinforcement learning

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