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)

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“

Abstract

A consensus network is a dynamic system that evolves in time. Consensus of complete network is reached if and only if . Has  been de demonstrated that a convergent and distributed consensus algorithm in discrete-time can be written as follows:

where 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 , where  is the Perron matrix of a graph with parameter . 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.

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)

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.

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

meaning “coalition C can cooperate to ensure that . 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 and mechanisms are behavioral constraints To avoid undesirable behaviors, we have to cut out some transitions. An effective social law . 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.

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.

Básicamente, he tratado de explicar brevemente las idesas de E. Ostrom, Premio Nobel de Economía del 2009, sobre el modelado de este tipo de sistemas y bajo qué condiciones las iniciativas de autoorganización pueden tener éxito. Juan Freire habla de lo mismo aplicado a urbanismo emergente.

Por si no hay copias  para todos o alguien de lo que no han asistido quiere descargar el documento, os lo dejo disponible aquí. Luego amplio este post para explicar alguna cosa más.

Y como siempre, cualquier comentario es bienvemido

Así que, para que no me vuelva pasar, os dejo aquí las 5 dimensiones que forman el acuerdo: contexto, participantes, creación, contrato y ejecución. Con cada una de ellas he añadido algunas consideraciones que se me iban ocurriendo. Seguro que ni son todas las que están ni están todas las que son, pero puede ser un comienzo.

Y como slideshare no copia las transiciones, lo dejo en un vídeo. Es sólo un minutín, así que no te aburirás mucho. También puedes descargar la versión en  PDF. Y, como siempre, te recuerdo que cualquier comentario será bien recibido.

(he cambiado el vídeo de sitio porque se cortaba el audio)