[ATmeeting] Mapa de las tecnologías AT/bundles

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Service Directory (Alberto Fernández)

Un directorio de servicios "tradicional" para el registro y búsqueda de servicios en la plataforma. Se aporta

  • independiente del lenguaje para la descripción de los servicios (OWL-S, WSMO, WSDL, SAWSDL, tags, keywords...) -> las descripciones se convierten a n lenguaje común (AT-GCM)
  • pensado como respositorio de los bundles desarrollados por el AT

=> pending: incluir el manifesto de los bundles como un lenguaje más en la descripción de los servicios para qe se pueda integrar en AT-GCM

Planning Bundles (Alejandro Torreño)

Servicio de planificación centralizada para poder construir agentes que sepan planificar. Formado por 3 bundles:

  1. parser: procesa descripciones en PDDL (2.1) y las transforma en objetos Java
  2. grounding: grounds el dominio de planning (elimina variables instanciándolas a todos los valores posibles). Construye un grafo relajado
  3. planning: proporciona el servicio de planificación propiamente dicho.

Estos bundles pueden servirnos para la composición de los servicios. Ya tenemos nuestra descripción en PDDL (creo que es la 3.0). SI el planificador "por defecto" no sirve bastaría con integrar dentro de otro bundle un nuevo servicio de planificación acorde con las características de nuestro dominio (abierto, dinámico, descentralizado)

¿Podría hacerse otro bundle que transforme de Drools a PDDL o directamente a los objetos Java? De esa forma podía usarse también en la parte de BDI con Jason.

Otra cosa interesante sería ver cómo envolver un planificador "normal" en un bundle. Podríamos hacer la prueba con el que está incluido en el servicio web. En ese caso se puede trabajar directamente con las descripciones en PDDL (sin traducir)

Negotiation Probabilística (Antonio Bella)

Permite modelar procesos de negociación dando una serie de parámetros para que un agente sea capaz de determinar el precio óptimo para una transacción en concreto. Es un proceso completamente informado

Técnicas incluídas en el bundle:

  • Best Local Expected Profit (BLEP)
  • Maximun Global Optimisation (MGO)

MMUCA (Jesús Cerquides)

Mixed multi-unit combinatory auction: Resolver subastas sobre procesos complejos de forma colaborativa (ej: células de fabricación flexible). Se incluyen materiales, herramienta y procesos. Las subasta se emplea para construir la mejor estructura posible -ej: demo eProcurement con milling machines-

Funcionamiento del bundle: los participantes le envian sus pujas y el bundle determina la combinación ganadora, con la que crea el workflow correspondiente en una institución electrónica.

Sería interesante comparar esto (y el trabajo de Jar y Merichel sobre CSP distribuidos) con las redes de consenso. Pero para eso antes necesitamos que las redes sean capaces de tener en cuenta varios atributos simultáneamente (de momento MMUCA está en el mismo caso y sólo optimiza precio). Debemos abordar eso cuanto antes para que sean usables. Si las dimensiones son independientes o si están correlacionadas positivamente creo que la convergencia estará también garantizada, pero ya con dos variables incompatibles el sistema ya no tiene solución para nosotros (oscilante) -> es necesario el tener un criterio de parada que se propague en toda la red (¿agentes monitores cuya presencia garantice el cumplimiento de ciertas propiedades o impida comportamientos no deseados?).

Sugerencia: el trabajo de Martí con los commitment managers pueden servir para la gestión del tiempo en MMUCA en una ronda independiente (en principio, es más sencillo para probar).

Combinational Auction (Matteo Vasirani)

Local search algorithm to determine winner in combinatorial auctions. Algoritmo estocástico

[ATmeeting] Advisory Board

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No voy a hacer una revisión de lo que se comentó en la reunión con el Advisoy Board. SImplemente recojo las ideas de cada workpackage que tienen que ver con mi trabajo y de las que pueden surgir colaboraciones y trabajos comunes, así como ideas para continuar haciendo cosas.
WP2. Norms
El WP de normas está usando Drools como motor de inferencia y Repast para la simulación. Para favorecer la integración, sería interesante usar también los dos.
  • Drools puede ser el componente principal para la composición dinámica de servicios
  • Repast permite visualizar el comportamiento de los agentes en la red. Además, como comentaron los de USal en la reunión de Ovamah, puede enlazarse con Weka para analizar los resultados.
Por otra parte, están mirando propiedades como la homofilia (la gente tiende a agruparse en grupos con características si,ilares) puede modelarse como normas. Sería una forma natural de 'forzar' a que los agentes se comporten de una determinada manera dentro de la red (tanto en los servicios como en las redes de consenso) y de que los agentes sean capaces de aprender que es mejor cooperar en la propagación de la información. Es una idea interesante para que Natalia y Elena escriban algo.
WP1. Semantics
Ninguna. Aunque aquí está incluido nuestro trabajo (incluso se ha hablado de homofilia) y la parte de directorios de Alberto. en los highlights.
WP3. Organizations.
Importante la noción de social welfare como criterio de maximización de la "utilidad" de una organización. Aunque no me gusta demasiado hablar en estos términos, puede ser útil para representar los conceptos de coherencia en redes de consenso.
Relacionado con las 3D-EI, tengo que continuar con Unity y viendo cosas sobre entornos 3D. Es algo que no tengo que dejar, así como la relación con metaversos (completar la petición en la UPV) -> haría falta integrar estas herramientas dentro de la plataforma (al menos la generación de los mundos usando shape grammars)
WP4. Argumentation
Ninguna idea tampoco. Claro que imagino que el ser la primera justo después de comer no ayuda. Demasiado revuelto e inconexo. Espero que a mi no me pase lo mismo. Claro, que tener 3 personas presentándolo todo es complicado. Además, el task leader no tiene mucha idea de tareas (es un paquete complicado de presentar y de relacionar). Los highlights han sido demasiado técnicos, como en una conferencia (no para un board).
WP5. Trust
Interesante el trabajo sobre la propagación de opiniones en grafos (Nardine y Jordi). Las estructuras son jerárquicas (dirigidas, sin ciclos -árboles-) y pueden combinarse para obtener una opinión global (interesante para las redes de consenso).
WP6. Tools
La parte de generación de código ha cambiado de MOFScript a Xpand ¿eso afecta a lo que estamos haciendo con Andromeda?
WP7. Infrastructure
Sobre el OS yo también tengo mis dudas (como Ricardo), al menos tal y como está planteado. Creo que una "revolución" sería un OS pensado para la ejecución de agentes y no de humanos, Sigo pensando que mi visión de agentes como usuario, organizaciones como grupos y servicios como procesos es más adecuada. Los agentes serían propietarios de sí mismos, con su propia identificación y responsables de sus acciones (agentes con suidad).
Agreement technologies software
AT Technology Customer App Type
At library (bundles) developers web services, stand alone
Development tools (gormas, eide, thomas, mgx) developers MAS
AT environment (ATE) sw agentes & end users p2p environment (services+organizations)

[CostAT] Trust as a Unifying Basis for Social Computing

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by Munindar Signh

Trust underlies all interactions among autonomous parties over many social relationships: casual, familiar, communal, organizational, practical.... But trust it use to be a internal characteristics and it can not be extrapolated outside a single application.

A social applications specifies and configure (i) roles,, (Ii) social interactions and (iii) additional constraints. And the elements we have available to model these systems (architecture) are components, connectors, constraints (over both of them) and patterns (that generalize its behavior). In the case of a social systems, they are
components >> individuals
connector >> social relationships
constraint >> reciprocal (ej Facebook)
patterns >> ....

The claim of this presentations is that trust is what flues in the relationships and it is how individuals and social relationships can be characterized. The sample> an agent (Toto) that acts as a middleware and can provide with trust the interactions among real people in different applications. That is, a layer which can be used in social apps (for instance) to measure the confidence on other users (humans) interactions (NOTE. a very close concept to the trust bundle proposed in AT)

[CostAT] Coherence-based argumentation models for normative agents

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by Sindhu Joseph

Abstract :
In this talk coherence-based models are proposed as an alternative to argumentation models for the reasoning of normative agents and normative deliberation. The model is based on Thagard’s theory of cognitive coherence and exploits the coherence relations that exist between claims and conclusion of arguments. A coherence-based model is intended to introduce more flexibility in the process of deliberation and agreement generation among normative agents. The basic coherence philosophy and what makes it interesting in the context of normative agents that deliberate to regulate a domain of interest are discussed.

This paper shows the application of coherence models to an argumentation model in a normative, regulated environment. I'm interested not in tris particular application, but in the coherence theory (Thagard).

Coherence estudies associations between pieces of information. It tríes to separate information in sets that mutually support the data. In some way, it can be consideres as a constraint satisfacción problem.

Different types of coherence can be identified: deductive, explanatory, deliberative, analoogous or conceptual, depending on the type of information. The Thagard model is a model of deductive coherence. It can be considered as a constraint satisfacción problem. But the main difference is that it does not try to maximize the partition (not the optimal -it is not needed to find a solution-)

Coherence applied to argumentation sees positive relates info as supporting arguments and negative weights as attacks to a claim.

Problem (general) How the coherence weights are calculated? Well, it is addressed in the questions: depends (roughly) on the number of arguments supporting a hypothesis.

Something interesting in the conclusiones: it can model different tupes of agente (utility maximizares, norm abiders, altruistic...) What about diferentt personalities? And a possibility for us: introduction of contexto as part of the future work.

More infomration, read Sindhu Jospeh PhD. thesis, "Copherence-Based Computational Agency"

Consensus Networks as Agreement Mechanism for Autonomous Agents in Water Markets

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Es el título de nuestro paper en las Jornadas que organiza el $latex im^2$ (Instituto de Matemática Multidisciplinar) de la UPV: Mathematical Models for Addictive Behaviour, Medicine & Engineering. El tema es el uso de redes de consenso para alcanzar acuerdos de forma descentralizada, aplicado en concreto a problemas de gestión de recursos hídricos. A continuación te dejo el resumen (en inglés) y las trasparencias de la presentación. En cuanto esté publicado dejaré también la referencia completa al artículo y, si puedo por temas de licencia, el enlace.

Abstract

The aim of this paper is to present a way of share opinions in a decentralized way by a set of agents that try to achieve an agreement by means of a Consensus Network, allowing them to know beforehand if there is possibilities to achieve such an agreement or not.
The theoretical framework for solving consensus problems in dynamic networks of agents was formally introduced by Olfati-Saber and Murray (2004). The interaction topology of the agents is represented using directed graphs and a consensus means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents in the network. This value represents the variable of interest in our problem.

A consensus network is a dynamic system that evolves in time. Consensus of complete network is reached if and only if $latex x_i = x_j \forall i, j$. Has  been de demonstrated that a convergent and distributed consensus algorithm in discrete-time can be written as follows:

$latex x_i(k+1)=x_i(k) + \varepsilon \sum_{j \in N_i} a_{ij}(x_j(k)-x_i(k))$

where $latex N_i$ denotes the set formed by all nodes connected to the node i (neighbors of i). The collective dynamics of the network for this algorithm can be written as $latex x(k+1)=Px(k)$, where $latex P=I-\varepsilon L$ is the Perron matrix of a graph with parameter $latex \varepsilon$. The algorithm converges to the average (or other functions) of the initial values of the state of each agent and allows computing the average for very large networks via local communication with their neighbors on a graph.
The convergence of this method depends on the topology of the network and its convergence is usually exponential. But sometimes it not needed to reach a final agreement on a concrete value. This proposal uses consensus networks to determine if an agreement is possible among a set of entities. Agents can leave the agreement if its parameters are out of the expected bounds, so the consensus network can be used to detect the candidate agents to be members of the final agreement. All this process is solved in a self-organized way and each individual agent decides to belong or not to the final solution.
To show the validity of the present approach, a water market is presented as case of study. The water market is a case of complex social-ecological system (SES), where centralized and hierarchical approaches trend to fail and self-organized solutions seems to be more sustainable in the long term (Ostrom, 2009). In general, agreements related to natural resource management involve very complex negotiations among agents. Water demands and regulation is a very complex distributed domain appropriated for MAS.
An important question is if this kind of markets requires some regulation or not. From an exclusively economic point of view the dominant strategy for agents in deregulated markets is not cooperative because each agent wants to maximize exclusively his payoff, and therefore they are not interested in the global and socially efficiency of the natural resources.

[AT workshop] Session 4

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Reputation and confidence for artificial intelligent entities. A cognitive approach
(Jordi Sabater)

Trust deals with uncertainly and risky situations. A little difference: reputation (very similar) is one of hte mechanism to build trust and it is a social element. How it is used in a computer-based systems? Three layers (approaches): security, institutional and social. Trust and reputation are meaningful in the social approach. If we have a storngly ruled system (institutional approach) we do't need trust, just to follow the rules. Then, a cognitive model of reputation is needed.

A social evaluation is the evaluation by a social entity of some property (mental, physical or social) related with been social. Reputation is then a voice (something that is said) about a social property. But agents do not have to beleave this reputation measures: agents (as people) has no responsibility about spreading social evaluations. When people believes what other people sais, then reputation matches with image (what an agent believes in, consideres as true facts).Reputation means communication and gossiping is the channel used to transmit reputation measures. Images and reputation are based on facts, which have two measures: value and strength -> repage mechanism.

This repage cognitive computational model has to be inserted in an agent. It is important that (i) reputation model can be isolated from other reasoning mechanisms (planners, decision making tools); and (ii) be proactive: do not wait to be asked about reputation, but provide information to the rest of elements. Using a BDI (beliefs, desires and intentions) model with multicotext logics and bridge rules to integrate the context of teh repage mechanism into the context of beliefs, desires and intentions. In the logic, the difference between images and reputation is a ¿reified? difference. An argumentation model is used

Psychopharmacology of agreement
(Adolf Tobeña)

There's lots of corrdination, obbidion, ... but few agreements among humans. ANd the second point of the speech is that humans need drugs. And these facts "llevan" to psychiatric aspects of agreement: why patients are more trending to cooperate/agree after been treated?

Usually, xanthines (caffeine, tobacco) are present during negotiations and bargaining processes. 5 years ago was demostrated that oxitocin increases trust in humas. Furthermore, they observed that participants trend to not change the trusting behaviour even after knowning they had been betrayed (50% trials) and the brain was actually don't responding as been betrayed (e.g. activity in brain areas related with dissgust).

booster drugs for agreements (prosocial, protrust)

  • alcohol, cannabinoids
  • xanthines, nicotine
  • oxytocine, prolactine, NPY
  • estrogens

and antiagreement drugs are (indice paranidogenic, autistic and antisocial behaviors)

  • cocaine, amphetamines
  • LSD, mescaline, psilocibine
  • androgens

But they've observe that testosterone had a possitive effect on human bargaining behavior.... and they did it on women!!!! They shown that one sunlingual dose os testosterone in women cause a substantial increasein fair bargaining, reducing cinflics and increasing efficiency on social interactions. ANd usinga placebo they demonstrate that was a real effect (the believed testosterone group behaves as the group without testosterone. And in men? Other group showed that high levels of testosterone (natural measuring) reject low (unfair) ultimatum game offers: $5/$40. Testosterone has influence in how the rest of the people consider others as leaders. Testosterone redcuces conciuos detrection of signals (face expressions) serving social correlations ->  a high probability of entering into a fight is related with risk/venturesome behavior (you accept more faces as neutral)

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[AT workshop] Session 3

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The neural basis of empathy and coordination
(Christian Keysers)

1.- feeling the intentions of others
The neurons involved in a concrete movement (grasping something), surprisingly, respond also when the action is seen (about the 10% of the neurons - mirror neurons-).  Interesting: you can "run simulations" in your mind and the brain behaves as if the real action is being performed. But, what happened if you see a not human (f.i. a robot) doing the same action?. The active areas in the brain of the observer are the same. That is, your brain is "learning" how to do this action.

How about sounds?. The set of neurons dedicated to do, see or hear something is different. In humans, experiments done where about to hear the result of actions performed by the hands or by the mouth (clearly separated in the brain). The correspondent motor areas are not activated, but the area that responds to the stimuli does.

SO, how do we coordinate each other? Because the coordinate system of the other doing an action is not our own coordinate system and the active area in the brain is different. The mirror system transform back and forth between sensory an motor representations, providing the basis for optimal coordination of observed and executed actions

2.- why do we cooperate?
It is related with emotional behavior. Experiments done with pleasant and disgusting smells. Again, the response of the brain is very similar when we feels disgust or when we see someone felling disgusted (by their expression in the face) And impairing simulation with real stimuli can damage the brain (so we cannot properly distinguish the correct emotion/sensation). Emotional simulation and empathy are linked too? It seems to be, and it is not exclusive for disgust. Pain in self and in others overlaps, but disgust and joy overlaps too, so it is difficult to identify the correct emotion. Any way, this facts motivate us to cooperate: we share the same things than others (empathy).

Cooperation and generosity
(Paul van Lange)

Generosity: behaving more cooperatively than the others. Noise refers to unintended errors that affect interaction outcomes. Noise is a matter of fact in social systems and undermines cooperation. But generosity can (or not) cope with noise.

To understand social situations one needs to understand dependence, interests and information availability (al least).imperfect information appears in partner preferences or discrepancies about outcomes and intentions (why he's not responding my emails?).

But the amount of generosity to apply has to be biased. The optimal balance between reciprocity, generosity o stingy has to be found (e.g. tit-for-tat: nice, forgiving, retaliatory and clear.... but it does not repair)

After a lot of results, seems that, under negative noise, generosity (i) build trust, (ii) pair well with reciprocity, and (iii) -I missed this one-. Besides: communication helps (when noise happens, inform the other -say sorry-); individuals copes with noise better than representatives and empathy is effective.

NOTA: ¿que ocurre si se introduce la generosidad como un factor  más en el demostrador mWater a la hora de gestionar las agrupaciones de usuarios autoorganizadas? Parece que puede ser una buena variable para mantener una gestión óptima en el problema de los comunes.

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[AT workshop] Session 2

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On the use of argumentation in agreement technologies
(Henry Prakken)

Agents need argumentation (i) for their internal reasoning and (ii) for their interaction with other agents. Explaining basic things about argumentation process: argument attacks. The situation of the dialog can be modeled in a graph colored by defining in and out arguments (Dung, 1995). And there is a sound and complete game that allows to determine if an argument is feasible or not without having to calculate the entire network: an argument A is feasible when there is a winning strategy for A follow.ing the game rules.

Problem: it is asumed that all information is centralized and static (a single theory -KB-) So dialogue game systems are developed. He's using the Walton & Kreebe dialogue types (without eristic :-) I've seen this a lot of times already.

An Interesting thing: blocking behavior (always asking why) It can be solve by using sanctions:
social sanctions (i wont talk you any more)
shift of burden of proof by a third party (referee): q since r // why r? // referee: you must defend not-r

I already knew most of these things (thanks to Stella)

"Prof. Kripke, let me introduce Prof. Nash", or
Logic for Automated Mechanism Design
(Mike Wooldridge)

In MAS the interaction is done by mechanisms = protocol + self-interest and agents are the participants in these mechanisms. So mech. can't be treated as simple protocols. (ex. sniping in eBay -bidding in the last 5 min. trying to be the last bidder-). A MAS can predict the sniping behav. of users in eBay?  The environment of an agent is a mechanism too, that contains other agents that act strategically to achieve their own goals

The formalization used is ATL (alternate-time logic), introduced in 1997 top analyze games. It defines a branching-time model as a graph and CTL is the logic used to talk about branching-time structures, extending propositional logic with path quantifiers (A,E) and tense modalities (F, G, X, U).

CTL sais when something is inevitable or possible, but it hasn't notion of strategy action nor agency (it's a problem to model mechanisms.... and service-based applications too). ATL is intended to overcome these limitations. The basic expression is

$latex \langle \langle C \rangle \rangle \phi$

meaning "coalition C can cooperate to ensure that $latex \phi$. The idea is that, using coalitions, we can model who is going to achieve a property (a coalition can be an individual entity or even an empty set -modeling 'nature'-). An example about social choice (voting) mechanism. Now, mechanisms can be validated.The logic can capture dependencies among agents, as stressfulness (all goals met), veto (j needs i to achieve its goal), mutual dependence (all agents are mutual dependence... veto relationship)

(note: but we can't model actions yet, so I guess it isn't useful for us)

A concrete application about social laws (normative systems). Objectives will be ATL formulae $latex phi$ and mechanisms are behavioral constraints $latex \beta$ To avoid undesirable behaviors, we have to cut out some transitions. An effective social law $latex (\phi,\beta) \models \phi$. But compute this is a NP-hard problem. An example with the typical train organization in a tunnel. But you cannot model just the properties you want to avoid. The properties you want to preserve have to be modeled too in order to have system doing useful things.

But, what to do with non-compliance? The idea isto incentive compatibility and, to do this, we need preferences (a prioritized list of goal formulae). I like this idea: the utility of the agent comes from this list, from a worst (and weak) rule to the best (and stronger) rule. For instance, related with resources, have it assigned often and for a long time.

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[AT Workshop] Session 1

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Towards the biological basis of cooperation
(Arcadi Navarro)

Talking about genome and human evolution. The interesting thing: the effects on social behavior.

After a very interesting introduction to genomic, begins trying to relate genetics with social behavior: because to cooperate can have some explanations in our genes (and this can be the explanation of why humans have  been a successful specie): genetic variability for behavioral traits is considerable. The problem is that this is very difficult to interpret. Fortunately, there are some genetics related with economic behavior that can be studied and replicated in labs.

Example: the ultimate game: people trend to make 50:50 offers and to reject less that 30% (not an reasonable decision from an economic point of view). But chimpanzees behaves as rational maximizes in an ultimatum game. Both species have evolve completely different behaviors. Why? we have to study this from a genetic perspective. -> agents playing games are as chimpanzees. And researchers are discovered that serotonin makes individuals to be more generous (just a joke: men have more serotonin than women). Or even between MZ twins, differences in the acceptance threshold in ultimatum game have been observed. Examples with more genes.

Measuring Strategic Uncertainly and Risk in Coordination-, entry-Games and lotteries with fMRI
(Rosemari Nagel)

Uncertainty can be classified as

  • exogenous (risk): know the prob. of all possible states of the world (objective prob.)
  • endogenous: in absence of endogenously given prob.;  -> strategic uncertainty (SU) e.g. outcomes depends on social interaction -games- (subjective prob.)

How brain solve individual or strategic uncertainty? Can we predict choices and brain activity in games?
Results: people behaves similarly in lottery and coordination games, but not in entry games. And the activity in the brain increases in lottery -> coordination -> entry. Some graphics about the different parts of the brain active while playing each type of game. Similar activity in entry games of risk lovers and risk averse people.

Summarizing, the entry games create mode strategic uncertainty as predicted by the nature of the mixed equilibrium which also involves levels of reasoning.

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mWater: a case study of AT

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mWater es el demostrador que tenemos asignado en el AT. Se trata de ver cómo usar los conceptos de acuerdos en un caso de gestión eficiente de agua.

Se aplica sobre un entorno en concreto (acuífero La Mancha Oriental), donde se identifica además la estructura de distribución del agua y la estructura social existente. La implementación se realiza sobre instituciones electrónicas (primer prototipo) y Thomas (sistemas abiertos). Actualmente se está desarrollando en mercado intra-cuenca para la gestión de derechos de agua modelada con Islander.

Básicamente, el proceso puede resumirse de la siguiente manera

  1. cuando los agentes entran en el mercado, deben registrarse en las escenas iniciales
  2. a continuación, pasan a un trading hall en el que pueden ver qué transacciones se están realizando
  3. las transacciones se realizan en un conjunto de trading tables. El agente selecciona en qué mesa(s) quiere participar.
  4. en las mesas se firman contratos entre partes donde se negocian derechos de agua
  5. los contratos deben quedar expuestos 30 días para que otros agentes afectados puedan efecturar quejas y alegaciones (grievance)
  6. Una vez firmado el contrato, cualquier incidencia se resuelve mediante un mecanismo de quejas.

mWater es un entorno regulado y esta regulación se realiza a través de normas. Son de 3 tipos

  • definidas por el gobierno (p.ej. PHN)
  • definidas por las asociaciones de regantes
  • normas sociales

Otro aspecto importante es la gestión organizativa de los agetes que participan en cada escenario: cómo comparten normas, como se participa en actividades reguladas, emergencia y consolidación de normas comunes, toma de decisiones colaborativa y estudio de la dinámica de la propia organización.

Dado que la gestión de conflictos se realiza mediante quejas, es neceario definir las tecnologías necesarias: protocolos, artuitecturas, ténicas de negociación y argumentación... Los conflictos surgen del hecho de que algún participante no cumple su parte del contrato, por lo que habrá que poder validarlos y detectar estas situaciones. Todo esto se instrumenta a través de acuerdos.

Acaba explicando qué tareas del AT están relacionadas con qué partes del mWater. Un poco liado para contarlo aquí... pero podéis verlo en las transparenicas.

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