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Ted Szuba: Chaos and collective intelligence

Tadeusz (Ted) Szuba is a pioneer of the formal study of the concept of collective intelligence (CI). Collective intelligence is group intelligence that emerges from the competition or collaboration of many individuals. It may involve Biology, Sociology, Computation or Business. In 2001, Dr. Skuba proposed a mathematical model for the phenomenon assuming to be a random, distributed and unconscious process. Recently, Professor Skuba has studied Chaos as one of the basic properties for CI. For instance, in bacterial colonies emerges CI based on mutual observation of metabolism products and the exchange of DNA. This phenomenon may vanish if a form of Chaos is not maintained. Professor Szuba asks himself what should the measure and form of Chaos be in the social structure. In case of society, Szuba speaks in terms of parallel Chaos. When individual intelligence of a being is poor, e.g., ants, bees, etc., it must be massive parallelism. It is obvious that increasing the organization of a social structure reduces chaotic behavior of individuals but reduces ability to solve unexpected problems, which a social structure can confront. In fact, panic behavior has a chaotic nature. In case of bacteria, for instance, this is some kind of mutation, while in case of human beings it can take any form of escape. According to Szuba, societies frequently add a chaotic element in an artificial way. For example, the concept of lottery giving an illusory chance to people, to change their life. Szuba concludes that the ability of the societies to solve specific class of problems, expressed through CI becomes non-Chaotic, despite a lot of individual Chaos inside the society.

Cynthia Breazeal and the Personal Robots Group

Cynthia Breazeal directs the Personal Robots Group at the MIT, being a pioneer of social robotics. She develops socially intelligent robots interacting and communicating with people. She wishes to help people of all ages contributing to their quality of life, in aspects such as health or learning and education. 

Socially aware robots require a broad range of capabilities in order to work together with human beings. These robots must be able to recognize intent and action supposing a challenge to the robotics community. It is a very difficult task to examine highly complex environments in the number of states, transitions and interactions. Machine learning methods like Reinforcement Learning or POMDPs do not scale to high-dimensional state-action spaces. Breazeal and her group think to endow the robots with human-like behaviour through generating functionally capable data-driven behaviours sourced from human behaviour. For it, they use of plan networks and case-based planning simulating crowdsourcing behaviour. So it is harnessed the wisdom of the crowd. They evaluate the resulting autonomous robot behaviours using a real-world reproduction of an online game environment and obtaining interesting results which optimize the results  about social interaction between humans and robots, obtained by traditional methods

My-Pet-Our-Pet

My-Pet-Our-Pet (see the article by Chen et al., 2007-http://www.petpartners.org/document.doc?id=289-) is an animal companion system based on open learner models as animal companions. The learners are grouped into several teams, in which each learner is surrounded by two kinds of animal companions, an individual animal or My-Pet, and a team animal companion or Our-Pet. My-Pet encourages users to learn individually online subjects through nurturing the pet character. The repertoire of activities in the system consists of four modes: pet nurturing, individual learning, game competition and group discussion. In an experiment in a classroom of one elementary school in Taiwan, 31 students were motivated and engaged in the process of raising their pets, and most of them (29 students) increased effort to improve their learning. Open learner models as animal companions benefit student´s learning.

Social Cognition and Collective Intelligence

Collective intelligence is the capacity of human collectives to engage in intellectual cooperation. There exists an interesting correlation between collective intelligence and the degree of the human development. Some months ago it was held a dialogue in MIT TV between Andrew Lo (MIT), Martin Nowak (Harvard), Alexander Pentland (MIT) and Rebecca Saxe (MIT). Thomas Malone moderated the dialogue. The interested reader may find the link here: http://video.mit.edu/watch/social-cognition-and-collective-intelligence-7792/

Living Technology and Artificial Intelligence

Life has always been a source of inspiration for technology. Nowadays protocells are being created including artificial plasmids that produce functional proteins. In fact, electronic chemical cells present information processing between the chemistry and electronics. Digital organisms combine self-assembly with genetic descriptions providing a basis for using evolvable technology.

What living technologies will be like in the future? We can imagine organisms with synthetic genomes to produce hydrogen in fuel cells. Also we can imagine self-assembling, self-correcting, and adaptive software solving many problems of lack of robustness in software. Autonomous and adaptive robots  will enable the elderly to live independently and with greater security.

The emergence of living technology will create new social processes. We can expect purely artificial technology to acquire life´s properties and outperform the current technology. This process will be a singular event in human history.

Pedagogical agents and Artificial Intelligence

Pedagogical agents, that is, animated, life-like characters in a computer-based learning environment are attracting research in Education. Students learn lessons while interacting with agents designed to apply instructional protocols. In fact, learners can receive from the agent verbal messages tipically provided by humans. In a certain sense, a learner might be able to hold a connection with that agent, just as in a classroom. It is recommended that a pedagogical agent look human-like looking for a peer-to-peer interaction over teacher-to-learner interaction. The value of interaction with equally able peers generates the cooperation of equal partners fostering thinking, intellectual development and even affect. By this, researchers have attempted to simulate this interaction in tutoring systems. For instance, Biswas and colaborators at the Vanderbild University with the project Betty´s brain. Betty is a kind of anthropomorphized learning companion building social relations with the student. A pedagogical agent is not a conventional computer-based approach. Traditional human-computer interaction approaches haven´t studied how to support social interaction. Antropomorphized learning companions are just the possibility for such that exploration. It is obvious that the social interaction with the teachers and the learner´s perception of them play an important role in influencing the motivation and the learner´s efficacy. So the social interaction with learning companions in computer-based environments.

PARO or the therapeutical use of robots

Eight years ago, PARO, a therapeutical robot developed by Takanori Shibata, began to be sold commercially. PARO has the appearance of a seal but also presenting characteristics of human babies. It has been used with dementia patients in Japan, Italy or Germany. In an experimental study, half of the dementia patients that interacted with PARO improved their brain activity. PARO can successfully create affective bonds with people. PARO is the best proof that for humans is easier to interact with robots different from what we experience. Humans feel uncomfortably interacting with mechanical objects very similar or that cannot be distinguished from themselves. Psychology uses toys in therapy and we can create artificial seals and project upon them the feelings that we ascribe to real seals. But being ascribed a property and having it are not the same thing. The uncanny hypothesis holds that when human replicas look and act almost, but not perfectly, like actual human beings, it causes revulsion among human observers. This is an interesting lesson for the development in robotics of the imitation myth because people interact better with a robot that does not provoke expectations of how it should behave.



Duncan Luce (1925-2012): in memoriam

(Powerpoint by Jean-Claude Falmagne in the University of Navarra,

 remembering to Duncan Luce (August, 30, 2012). (Photo by Carlos Pelta)

Duncan Luce died August, 11, 2012, in Irvine (USA). Professor Luce has been one of the most renowned mathematical psychologists in 20th century. Pioneer of the quantitative formalization in Psychology and of the rigorous analysis of the Theory of Measurement, I remember the deep impact of his book Individual choice behavior on my youhtful mind. It was fascinating to see how Luce´s version of the principle of the independence of irrelevant alternatives was exploited in new and surprising directions. Choice probabilities for different sets of alternatives satisfying the principle of independence of irrelevant alternatives might  be consistently defined independently of the actual choice set.

I was lucky to shake hands with Professor Luce in the the "2011 Meeting of the European Mathematical Psychology Group" at  Paris one year ago. Requiescat in pace, Professor Luce.

EMPG 2012: Mathematical Psychology in the University of Navarra

                      (Access the University of Navarra) (Photo by Carlos Pelta)                                                

The annual Meeting of the "European Mathematical Psychology Group" (EMPG 2012) has been held at the University of Navarra (Pamplona, SPAIN), 29-31 August, 2012, and has been a great success. The key of this success has been Professor Christine Choirat, chair of the Meeting, whose excellent organization has been acknowledged by all concerned. Thanks to Professor Choirat and to the colaborators of the Economics Faculty and thanks to the University of Navarra for receiving so favourably this Meeting. The next Meeting will be celebrated in Postdam (Germany).

Between some of the most interesting talks I will emphasize the following:
On Wednesday, Professor Jacqueline J. Meulman  (Leiden University) spoke in a plenary talk about the new approach developed in Leiden called nonlinear multidimensional data analysis. Professor Noventa (University of Padua) presented in colaboration with Professors Stefanutti and Vidotto, an analysis of item response theory and Rasch models based on the most probable distribution method. Professor Budescu (Fordham University) analyzed test-taking behavior showing that penalties for incorrect answers have detrimental effects for both Test-Takers and Test-Makers. Professor Hudry (Telecom ParisTech) showed the NP-hard nature of the computation of a linear order or of a complete preorder under remoteness conditions. Professor Doignon (Brussels) described a geometric interpretation of therelationship between the probability distribution on knowledge states and the derived distribution on response patterns. Professor Núñez-Antón in colaboration with Professors Arostegui and Quintana, talked about the comparation of the outcomes of eight techniques of logistic regression for studying data from depressive patients. Professor García-Pérez (Complutense University of Madrid) proposed a solution for the problem of residual analysis in contingency tables. Professor Carlos Pelta presented his computational system based on agents (PSICO-A) for teaching Psychology and the journey was closed by Professor Farina (Siena) with an experiment about choice reversal in anticipatory feelings.
On Thursday, Rutherford (Keele University) developed a plenary talk about methodological problems concerning to the hypothesis to be tested in Psychology. Professor Colonius (University of Oldenburg) analyzed his Universal Fechnerian Scaling technique, a method for computing subjective distances among stimuli from their pairwise discrimination probabilities. In the afternoon Professors Erber, Goebel and Nan from Vienna, presented their systems for pattern recognition training by using visual feedback to the amputee.
Undoubtely the most emotive presentation of the Meeting was developed by Professor Jean-Claude Falmagne (University of California, Irvine), pioneer of the Mathematical Psychology and founder member of the European Mathematical Psychology Group. Professor Falmagne remembered the contribution and aspects of the life of the late Professor and friend Duncan Luce, one of the most important mathematical psychologists in 20th century.
Professor Alcalá-Quintana described an extension of her indecision model in psychophysics for producing second choices that are consistent with empirical data obtained under a second-choice paradigm without resorting to the increasing-variance assumption. The model uses a proper two-alternative forced-choice (2AFC) task with a three response format. Just at that moment Professor Laming (University of Cambridge) established a correlation between the 2AFC paradigm and data corresponding to psychophysics.
On the last day Professor Pigozzi (Paris Dauphine) developed in an interesting plenary talk her application of the labeled deductive systems theory to the psychological aspects of the argumentation. Professor Suck (Universuty of Osnabrück) introduced his investigations about set valued random variables and Professor Induráin (UPN) spoke about results concerning to separability properties relative to semiorders (results obtained in colaboration with Professor Estevan and professors Candeal and Gutiérrez-García). Finally Professors Stefanutti, de Chiusole and Spoto (University of Padua) introduces their ideas on the basic local independence model, a restricted latent class model for probabilistic knowledge structures.

European Mathematical Psychology Group Meeting (EMPG 2012)

Organized by Professor Christine Choirat (Universidad de Navarra), and with a Scientific Committee formed by Professors Bouyssou (France), Colonius (Germany), Doignon (Belgium), Falmagne (France and USA), Hudry (France), García-Pérez (Spain), Marchant (Belgium) and Induráin Eraso (Spain), it will be held at the University of Navarra (Pamplona-Spain), the annual Meeting of the European Mathematical Psychology Group (EMPG-2012), from August 29, 2012 until August 31, 2012.

The plenary speakers will be Professors Meulman (Leiden University), Pigozzi (Paris-Dauphine) and Rutherford (Keele University).

The author of this blog, Carlos Pelta, will attend the Meeting with a talk entitled "PSICO-A: A new computational system for learning Psychology". Below the program is available:

https://sites.google.com/site/empg2012/program

Neurosciences Madrid 2012

Organized by Ramón Areces Foundation and coordinated by Professor José Luis Muñiz Gutiérrez (CIEMAT), a series of lectures will present the outlooks from different disciplines and perspectives about Neuroscience from Wednesday, July 4, 2012 until Tuesday, July 5, 2012. Herein I enclose the program of this Meeting:

Program

Coordinated by:
José Luis Muñiz
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT).
Grupo de Física Médica de Real Sociedad Española de Física (RSEF).

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Wednesday, 4

09:30 Opening

Raimundo Pérez-Hernández y Torra
Fundación Ramón Areces.

María del Rosario Heras Celemín
Real Sociedad Española de Física. (RSEF).

José Luis MuñizGrupo de Física Médica de la Real Sociedad Española de Física (RSEF).

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10:00 La neurona de Jennifer Aniston

Rodrigo Quian Quiroga
Department of Engineering. University of Leicester. Reino Unido

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10:40 Estudio de la conectividad funcional en registros de alta densidad: cuando más es menos

Ernesto Pereda
Departamento de Física Básica. Universidad de La Laguna. Tenerife.

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11:20 Redes Complejas y epilepsia del lóbulo temporal. Focalizando fuera del foco la causa de las crisis focales

Guillermo Ortega
Hospital Universitario de La Princesa. Madrid.

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12:00 Break

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12:30 Sueño, conciencia y complejidad

Enzo Tagliazucchi
Goethe-University Frankfurt. Alemania.

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13:10 Visualización microscópica del cerebro desde los tiempos de Cajal hasta nuestros días

Javier de Felipe
Centro de Tecnología Biomédica (CTB). Universidad Politécnica de Madrid.

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13:50 Break

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16:00 Ritmos cerebrales buenos y malos: estudiando la dinámica neuronal normal y epiléptica

Liset Menéndez de la Prida
Instituto Cajal. CSIC. Madrid.

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16:40 La física del "dolce far niente", ¿Qué hace el cerebro cuando no hace nada?

Dante Chialvo
Neurophysiology Laboratory. University of California, Los Angeles. EE.UU.

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17:20 Aplicaciones clínicas de la Imagen Médica en la psiquiatría/salud mental

Celso Arango
CIBERSAM. Hospital General Universitario Gregorio Marañón. Madrid.

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18:00 Discussion

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Tuesday, 5

10:00 Neuroimagen por Resonancia en Enfermedades Neurodegenerativas y Neurológicas

Juan Antonio Hernández Tamames
Universidad Politécnica de Madrid.

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10:40 Neuroimagen estructural en adolescentes con psicosis y autismo

Joost JanssenCIBERSAM. Hospital Universitario Gregorio Marañón. Madrid.

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11:20 Interfaces Cerebro-Máquina: aplicaciones básicas y clínicas

José M. Carmena
Brain-Machine Interface Systems Laboratory.
University of California, Berkeley. EE.UU.

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12:00 Break

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12:30 Estudio de la recuperación del daño cerebral mediante MEG

Nazareth P. Castellanos
Laboratory of Cognitive and Computational Neuroscience. UCM-UPM. Centre for Biomedical Technology.

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13:30 Inmunología y Sistema Nervioso: conceptos básicos y aspectos clínicos

Juan Antonio García Merino
Hospital Universitario Puerta de Hierro Majadahonda.

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13:50 Break

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16:00 Células Madre neuronales. Caracterización electrofisiológica y desarrollo de una terapia celular para el tratamiento del Ictus Isquémico

Josefina María Vegara Meseguer
Universidad Católica San Antonio de Murcia.

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16:40 Terapia celular en Esclerosis Lateral Amiotrófica: del laboratorio a la clínica

Jonathan Jones*, Mª Carmen Viso, Diego Pastor, Salvador Martínez
(*) Instituto de Neurociencias. Universidad Miguel Hernández. San Juan, Alicante.

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17:20 Nuevas perspectivas en neurorregeneración: terapia celular aplicada a la discapacidad neurológica

Jesús Vaquero
Servicio de Neurocirugía. Hospital Universitario Puerta de Hierro Majadahonda

PoCoMo: playful social interactions between multiple projected characters

PoCoMo (see http://fluid.media.mit.edu/people/roy/media/PoCoMo-cam-ready-optimized.pdf) consists of two mobile projector-camera systems with computer vision algorithms to support the behaviours of characters projected in the environment. The characters are guided by hand movements and can respond to other characters, simulating a reality of life-like agents. Users hold micro projector-camera devices to project animated characters on the wall and the characters recognize and interact with one another. Extracting visual features from the environment, an algorithm enables operations in a limited resource environment. The system creates games including social scenarios of relating and exchange between to co-located users. The characters are programmed to have component parts with separate articulation and with different sequences based on the proximity of the other characters. The projected characters can respond to the presence and the orientation of one another and acknowledge each other. Also they can trigger gestures of friendship such as shaking hands. Characters can leave presents to one another. Each character has an identity that is represented by the color of its markers. A detection algorithm scans the image by applying a threshold extracting the contour of the figures. The detection algorithm has been implemented in C++ and compiled to a native library. The UI of the application has been implemented in JAVA using ANDROID API. In a future work, the authors (Shilkrot, Hunter and Maes from MIT Media Lab) will integrate markers with the projected content and migrate the application to devices with wider fields of views.

Origin of epistemic structures and computational agents

Adding cognitive structures to the world is a basic adaptive strategy. Chandrasekharan and Stewart (2007) developed an interesting perspective modeling swarms of foraging robots for simulating epistemic structures in agents. Epistemic structures could be structures generated for oneself (for instance, bookmarks), structures generated for oneself and others (pheromones, etc.) and structures generated exclusively for others (warning smells...) (see Chandrasekharan and Stewart, 2007, 331). A key feature of such structures is their task-specifity. According to Chandrasekharan and Stewart (art. cit., 332), organisms sometimes generate random structures in the environment and organisms have a bias to reduce physical or cognitive effort. The authors design a computational simulation consisting of an environment of a 30x30 toroidal gridworld, with one 3x3 square patch representing the agent´s home, and another representing the target. This target can be thought of as a food source. An agen can perform several possible actions: moving randomly; to distinguish between their home and their target (consider models of ant foraging and postulate a home pheromone and a food pheromone) and to modify the environment. The agents have four sensors, two external and two internal and are programmed by a genetic algorithm to evolve foraging behavior in the agents. The fitness function of the genetic algorithm is the inverse of the time measure, which is interpreted as an expression of tiredness. In the simulation, 10 agents foraged at the same time. Initially, the agents behaved randomly. Most agents did not find the target. On average, each agent was completing 0.07 foraging trips every 100 time steps. After a few hundred generations, the agents were completing an average of 1.9 trips in that same period. The result confirmed that the agents were able to systematically make use of their ability to sense and generate structures in the world, on an evolutionary time scale. But is possible for the agents adding structures to the world within their lifetimes? The Q-learning (Watkins, 1989) is the simplest method to perform this task. Using the Q-learning algorithm, 10 agents were ran for 1,000 time steps. To indicate tiredness, it was gave them a reinforcement value of -1 while foraging. When they returned home after finding the target, they were given a reinforcement of 0. By the end of the simulation, agents required only around 150 time steps to make a complete trip (a foraging rate of 0.66 trips in 100 time steps, that is, twice as quick as agents without the structure-forming ability). Even they spent 58% of their time generating structures: epistemic structures generation allowed the agents to complete their foraging task down from 300 time steps. These simulations facilitate an integration of the symbolic and situated views of cognition supporting the extended mind thesis.

Genetic Programming and self-replicating structures in cellular automata

The study of self-replicating structures is a very important field in Artificial Life. Cellular automata have studied two kinds of replicating structures: self-replicating ones and universal constructors. Von Neumann designed complex universal constructors consisting of multiple components. A second type of replicators, self-replicated loops, were studied by Langton, showing that looplike structures used in universal constructors could independently reproduce themselves. The replication process underlying both universal constructors and self-replicating loops, uses a sequential construction in which an arm extends from the parent structure and deposits the child structure. It depends on manually programmed sequential instructions depending on the presence of totalistic transition functions. But today Genetic programming facilitates the evolution of cellular automata given initial structures where cells may have several possible states. Pan and Reggia (2010) obtain replicating structures that are qualitatively different from past manually designed universal constructors and self-replicating loops. As a consequence, it is possible to produce many replicators that vary in just a single property.

To build a Genetic programming system that can program a cellular automata to support self-replication, the authors use trees as data structures ("chromosomes") that represent both structural information and the rules forming the state transition functions. They use a fitness function that generates the ocurrence of multiple copies of an initial structure in the cellular space over time. These self-replicating structures produced by Genetic programming are different from those found in self-replicating loops and universal constructors: there is no an identifiable instruction sequence and no construction arm. An initial structure grows and then divide, making replication very fast. The structures move and it is not possible distinguish between parents and childrens. In some ways, their fissionlike replication process is similar to the splicing during mitosis in biological cells. Replicators can also support the construction of secondary structures as they replicate, either with or without a given initial seed structure. When the replication rules are executed in parallel in each cell, they often employ a strategy that has not been manually created in old constructions. For instance, the moving-wall strategy consisting in a line of replicating structures depositing secondary structures behind it. Thus, Genetic programming is a very powerful tool in the discovery of novel self-replicating structures in cellular automata.

A computational simulation of laws of imitation in Social Psychology

(Spatial prisoner´s dilemma for b=1.6)

I am going to explain the design of a gamed based on the spatial prisoner introducing the three laws of imitation defined by the French Sociologist Jean Gabriel Tarde. It was presented by Carlos Pelta in the "2011 Meeting of the European Mathematical Psychology Group", celebrated in Paris.

The first law or law of Close contact (LCC) describes how individuals in close intimate contact with one another imitate each other´s behavior. The second law of imitation or imitation of superiors by inferiors (LSI) establishes people follow the model of high status in hopes their imitative behavior will get the rewards associated with being of a "superior" class. Tarde´s third law is the law of insertion (LOI): new acts and behaviors are superimposed on old ones and subsequently either reinforce or discourage previous customs. The following imitation rules are introduced: (1) Conf rule (Conformist rule) simulating the law of Close contact (LCC): if your behavior is different from that of the neighboring agent, copy its behavior; (2) Maxi rule (Maximization rule) simulates the law LSI and is so defined: if the neighbor agent gets higher payoffs, copy its behavior; (3) Fashion rule: copy the behavior with the highest frequency of appearance in your neighborhood (in case of equal frequency, copy at random); (4) Snob rule: copy the behavior with a lower frequency of appearance in your neighborhood (if the frequency of behavior appearance is the same, copy at random). Rules (3) and (4) simulate the law of insertion (LOI), alternating the copy of the latest choice made with the Fashion rule and the copy using the Snob rule in every round of the game. The agents have memory for these two rules for the 3 previous rounds of the game.

Once taken into account all these rules in a spatial prisoner´s dilemma, and combining all the possible values of b between 1 and 1.9, with an initial distribution of cooperators between 0.1 and 0.9, a memory M between 1 and 9 rounds for the rules (3) and (4) and changing the number N of agents and the number of rounds of the game, it is concluded that in our game the imitation rules by Tarde yield a preferential attractor and a low proportion of cooperating individuals. Although we have introduced two rules of stochastic nature (3) and (4), its effect is nullified by the proper mimetic dynamics, which means that they can not even be present in the attractor. Thus, agents attracted by non stochastic rules, and b values that increasingly are encouraging defection, are mass defined as defectors which find ways to maintain their payoffs as high as possible. But this circumstance supports Tarde´s law LSI because the imitation of the agents with higher payoffs (defectors) is majority also including the case with an initial rate of 0.9 cooperators receiving a payoff of 1 (defectors receive payoffs from 1.1 to 1.9). Besides our simulation verifies the law LOI, combining rules (1) and (2), because the most imitated behavior or Maximization behavior, makes via rule (1), the new behavior reinforced, discouraging the cooperative behavior of the agents with lesser payoffs.

The dynamics of group affiliation

Today we present in this blog the work by Nicholas Geard and Seth Bullock about the dynamics of group affiliation-see http://eprints.ecs.soton.ac.uk/21195/5/S0219525910002712.pdf-. Models about group formation are common in many social simulations. But models on group affiliation in which individuals can belong to multiple groups simultaneously are very infrequent. According to Geard and Bullock (art. cit., 2010, p. 501), some types of groups may be exclusive, that is, membership in one group precludes membership in other groups of that type and others are non-exclusive. Affiliation with a group involves the consum of time and energy being very important to determine the degree of commitment of the subjects and their degree of participation in other groups for studying the social evolution.

In pre-modern societies the affiliations were made in a series of concentric social circles from family to the country but in contemporary society all is more complex. In the "liquid society" (so called by Zygmunt), the bonds in choices of affiliation are very complex and fuzzy. Individual may belong to multiple groups simultaneously and Geard and Bullock design a model of affiliation to non-exclusive groups. Their simulation considers a network of n nodes and m undirected edges (art. cit., p. 507), representing individuals and the social ties between them. Each node i has a trait vector of dimension d, representing that individual´s location in social space, a list of affiliated groups and a time and energy capacity. Trait values are bounded between zero and one and are uniformly distributed. The social distance between two individuals is defined as the Euclidean distance between their trait vectors. Each group has a cost of time and energy associated with being a member, reducing the number of groups with which a node can be affiliated.

In the network, edges may be rewired either to nodes sharing a common state, or at random. Nodes may either initiate a new group, or be recruited to an existing group by one of their network neighbors. A node initiating a new group will always leave existing groups to maketime for the new group while a node being recruited to a new group will either leave existing groups or refuse the recruitment attempt, depending of the sociodemographic space.

For the simulations, the authors explored the circumstance where all memberships are exclusive, the population evolving to a "connected community structure" (art. cit., p. 509), that is, a type of continuing connectivity combined with the occasional initiation of novel groups. All groups had a cost of one but increasing cost above one had relevant effects on network structure, decreasing the level of community comparable to that of a random network. this trend suggests that as individuals belong to more groups, they are lees likely to become disconnected from the population, but have more opportunities to leave groups containing different to themselves. Obviously, less costly groups were maintained in the population in greater quantities than more costly groups but the mean size of the more costly groups remained constant as capacity of time and energy increased, while that of the less costly groups grown rapidly.

One interesting prediction is that less costly groups may find it easier to thrive, but that more costly groups may retain more diversity. We believe that the ideas surrounding the simulation by Geard and Bulloch is an interesting step forward for the modelization of the complex problem of the affiliation in social dynamics.

EMPG 2011: Forty years of the "European Mathematical Psychology Group"

(Telecom-Paris) (Photo by Carlos Pelta)

The "Meeting of the European Mathematical Psychology Group", which was held at the Telecom ParisTech, August 29-31, 2011, was a great success. Major credits go to Professor Olivier Hudry, the Meeting chair, who opened the Meeting with a few welcoming remarks. Next, Professor Marchant, the first plenary speaker, shared the latest developments about "Measurement theory with unary relations". H. Colonius and S. Rach have developed an approach based on the theory of Fechnerian Scaling for the measure of visual-auditory integration efficiency. Fechnerian Scaling deals with the computation of subjective distances from their pairwise discrimination probabilities. In the afternoon, L. Stefanutti spoke about knowledge structures extending the probabilistic framework to represent local independence among items in a probabilistic knowledge structure. Professors Alcalá-Quintana and García-Pérez introduced a model of indecision in perceptual detection tasks revealing strong order effects that vary in sign and magnitude in a systematic manner across observers. Besides, they used a probabilistic model of temporal-order perception to provide a common framework for synchrony judgments. Professor Shanteau described his experiments on memory-retrieval versus decision-making in repetition priming. Finally, Professors Albert and Hockemeyer analysed the very relevant contributions by Jean-Claude Falmage, the founder of the "European Mathematical Psychology Group", to Mathematical Psychology.

On Tuesday, Professor Raijmakers started the morning sessions with the oral presentation entitled "The application of latent Markov models in category learning". Latent Markov models allows for analysing multiple latent categorization strategies separately in a robust way. Next, Professor Pelta introduced a computational simulation in Social Psychology, adding to the spatial prisoner´s dilemma the three laws of imitation formulated by Jean-Gabriel Tarde in his book "The laws of imitation" (1890). Professor Thiel exposed how automata network models can simulate the halo effect in human attitudes, using a connectionist model on the Beckwith and Lehman multiattributes theory. In the afternoon, Jean-Claude Falmagne presented the idea of "Learning Spaces" and his colaborator Eric Cosyn introduced a very interesting practical application. Cosyn has extracted 350 items forming a learning space whose domain is the field of middle-school algebra. Professor Induráin tried to establish a common theory that relates the different mathematical properties that the concept of "mean" can have.

On Wednesday 31 August, Professor Choirat reviewed her work on separable representations in Mathematical Psychology and decision making. Finally, I would like to stress the oral presentation by Professor Doignon about representations of interval orders.

A post-conference edition of Meeting presentations should be available on the journal "Electronic Notes in Discrete Mathematics" perhaps during the first quarter of 2012.

We are very grateful, in first place, to the city of Paris, and, in a second place, to Professors Hudry, Lobstein, Charon and Choirat and Telecom ParisTech, for the organization of the Meeting.

2011 Meeting of the European Mathematical Psychology Group (Paris)

The "2011 Meeting of the European Mathematical Psychology Group" will be held at the TELECOM ParisTech, August 29-31, 2011 (http://www.telecom-paristech.fr/eng/home.html).

The conference is organized by Irène Charon (Tèlècom ParisTech), Olivier Hudry (Tèlècom ParisTech and CNRS), Antoine Lobstein (CNRS and Tèlècom ParisTech) and Hayette Soussou (Tèlècom ParisTech). The Program has been elaborated by Professor Hudry and the plenary speakers will be T. Marchant ("Measurement theory with unary relations"), L. Stefanutti ("When the correspondence between probabilistic and set representations of local independence becomes a requirement: constant odds models for probabilistic knowledge structures"), D. Albert and C. Hockemeyer ("JCF´s impact is not limited to the foundation of the EMPG"), M. Raijmakers ("The application of latent Markov models in category learning"), J.-C. Falmagne ("Learning spaces in real life. How the large size of actual learning spaces guides the development of the theory"), C. Choirat ("Separable representations in mathematical psychology and decision making") and A. Diederich ("Optimal time windows: Modeling multisensory integration in saccadic reaction times").

In the parallel sessions, the author of this blog (C. Pelta) will started the morning sessions on Tuesday 30 August (10:30 h.) with his oral presentation entitled "Spatial prisoner´s dilemma and laws of imitation in Social Psychology". I design a game based on the spatial prisoner introducing the three laws of social imitation defined by Gabriel Tarde in his book Les lois de l´imitation (1890). The French author described (a) the law of close contact (individuals in close intimate contact with one another imitate each other´s behavior), (b) the law of imitation of superiors by inferiors (people follow the model of high status in hopes their behavior will procure the rewards associated with the "superior" class) and (c) the law of insertion (new behaviors reinforce or discourage previous customs). I run a computational simulation in which the formation of little "clusters" of cooperators supports not only the laws of Tarde but also the ideas of Sutherland which explain the imitation of deviance behavior as a process of communication within intimate personal groups or "differential association".

I predict that the Meeting will be a great success and that the organization will be very succesful. The readers of this blog are cordially invited to participate. On September it will be published in this blog a summary exposing the main ideas of this event to celebrate in Paris. For more information, please, see the webpage content designed by Professor Olivier Hudry (http://www.infres.enst.fr/~hudry/EMPG/).

Computational Models of Human-Mate Choice and KAMA

Since classical article by Gale and Shapely (1962), several computational models about Human-Mate Choice have emerged. In this article, the authors developed a "match-making" algorithm for a population with an equal number of males and females. Kalick and Hamilton (1986) found a correlation in physical attractiveness among married couples. Kenrick et al. (2000) used dynamic social influence networks and concluded that males are inclined to take advantage of unrestricted relations whereas females prefer restricted relationships. Other models have been presented but in this article for the blog, we expose perhaps the most recent model. And for the author of this blog, perhaps the most interesting. It is adequately complex (it uses a vector of values to simulate the population-level effects of the modification over time of particular characteristics of individuals) and employs the mechanism of computational temperature for the simulation, that is, the amount of energy that people put into encountering and dating potential mates). Bob French and Elif Kus (2008) (see their article that was published in the journal Adaptive Behavior, http://leadserv.u-bourgogne.fr/files/publications/000261-kama-a-temperature-driven-model-of-mate-choice-using-dynamic-partner-representations.pdf) distinguish between "parallel" versus "serial" decision-making procedures. The male selects someone to ask out among a number of available alternatives ("parallel" decision process) and the female then accepts or declines his invitation immediately upon receiving it ("serial" decision process). KAMA, the computational model designed by French and Kus, implements the search of resources for a mate by a feedback-driven internal parameter called "temperature". In KAMA each agent has its own temperature that regulates its behavior. Temperature is a function of both an individual´s recent dating history and his/her age (French and Kus, 2008, p. 75), that is, a measure of the energy that one is willing to expend to find a partner. The higher the temperature, the more willing an individual is to explore for a mate; the lower the temperature, the less willing he/she is to do so. Also KAMA is a "stochastic model: essentially all choices are made probabilistically, on the basis of the individual´s temperature. The authors run a simulation (20 runs of the program) starting with 600 indviduals (half of them, females) whose ages vary randomly between 18 and 48. Both males and females maintain a list of all previously encountered individuals and the values of their characteristics, updated with each new encounter. After acceptance or refusal of a date, the temperature of the individuals involved is updated. The mechanisms of KAMA include "attractiveness" implying mate value. Characteristic preferences for the profiles are "kindness and understanding", "exciting personality", intelligence", "physical attractiveness", "good health", "adaptability", "creativity", "desire for childen", "College graduate", "good earning capacity", "good heredity", "good housekeeper" and "religious orientation". In addition to their preference profiles and characteristic profiles, all indviduals maintain a memory of all individuals they have previously encountered, along with the values of the characteristics of these individuals that they have discovered through encounters and dates with them.

To test KAMA, French and Kus drew on empirical data from the Eurostat. In KAMA, physical attractiveness decreased with age and wealth. On average, males´preference weighting for physical attractiveness was higher than the preference weight for females. The most surprising results were that when males and females had identical preference profiles and identical temperature curves, there was a marked male-female hazard-rate shift. Why does the fact that males ask women out and women accept or refuse lead to this difference in hazard rates? The asymmetry in the males-ask/females-decide custom produce this difference in hazard rates. When women can ask men out, this asymmetry disappears and, all other things being equal, the male-female hazard-rate shift disappears.

More sophisticated versions of this model are necessary but we think that KAMA incorporates novel features like the notion of agents with indidualized preferences or the idea of computational temperature which controls the focus of decision making. Undoubtely, KAMA is a very functional and complete model for the Human-Mate Choice.

(Photo: Bob French).