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This paper presents a first study to apply a computational approach to Genito-Pelvic Pain/Penetration Disorder (GPPPD) using a Fear Avoidance Model. An adaptive temporal-causal network model for fear avoidance was designed and therapeutic interventions were incorporated targeting one or two emotional states. Validation with empirical data shows that for one type of individual therapeutic intervention targeting two states can reduce pain and other complaints. For three other types of individuals, targeting two emotional states was not sufficient to reduce pain and other complaints. The computational model can address large individual differences and supports the claim that interventions for GPPPD should be multidisciplinary.

In this study, a second-order adaptive social-cognitive agent model is introduced to examine prisoner recidivism. For comparison between different kinds of prisons and prison policies, recidivism rates from Norway and the USA are used. Two scenarios were used to model the effects of environmental, prison-related, and personal influences on recidivism rates. The presented adaptive social-cognitive agent model is based on a second-order reified network model. The model allows to computationally explore the effects on prisoner recidivism and the learning process for a prisoner’s social-cognitive state of mind as a main determinant of recidivism risk.

In this paper the effect of a humor therapy is modeled based on a Network-Oriented Modeling approach. Humor therapy is a mindfulness therapy which has been used since many years ago, when Abu Bakr Muhammad ibn Zakariya al-Razi (https://en.wikipedia.org/wiki/Muhammad_ibn_Zakariya_al-Razi) as a Persian scientist who used humor theory to distinguish one contagious disease from another, to make stressed individuals more relaxed. The presented adaptive temporal-causal network model addresses the computational modeling of humor therapy for a person who in the first step triggers two incongruent beliefs in order to get the humor from a humor context to overcome an ongoing stressful event. This happens by showing a comedy movie. As a result, the stress level in the body reduces. Hebbian learning is incorporated to strengthen the effect of the humor therapy.

Over the years, a clear and steady rise can be seen in the interest in veganism. Although research has been conducted to determine the reasons why veganism has grown, ultimately there is still a necessity for further research on how social networks affect its growth. This paper aims to provide a possible explanation for the rise in interest, using computational analysis based on a temporal-causal network model focussing on social contagion. This model portrays a simulation of a sample size population on Instagram, showing how a social influencer can influence the opinions of people directly (influencers’ followers) and indirectly (followers of the influencers’ followers), and how this compares to a situation in which this influencer is not there.

In this paper an adaptive computational temporal-causal network model is presented to analyse the dynamic and adaptive relationships between cannabis usage, anxiety and sleep. The model has been used to simulate different well-known scenarios varying from intermittent usage to longer periods of usage interrupted by attempts to quit and to constant usage based on full addiction. It is described how the model has been verified and validated by empirical information from the literature.

In this paper, a temporal-causal network model is introduced for a burnout in relation to sleep. The network model approach shows the impact of different lifestyle, personal and job factors on the development of a burnout. This model, for instance, can be used to schedule night shifts in order to preserve the needed recovery of exhaustive, irregular sleeping patterns or to investigate the effects of certain in lifestyles induced triggers on burnout.

Adaptivity in emotion regulation strategies has always been considered as one of the key factors for health. As the choices of emotion regulation strategies change as per context, the priorities of strategies also change with time. This phenomenon is called plasticity. This paper focuses on network-oriented modeling of the concept of metaplasticity from recent neurological literature which controls the plasticity. Simulation results are presented for the elaboration of the concept in choice of emotion regulation strategies with age.

In this study, the forming of social communities and segregation is examined through a case study on the involvement in the queer community. This is examined using a temporal-causal network model. In this study, several scenarios are proposed to model this segregation and a small questionnaire is set up to collect empirical data to validate the model. Mathematical verification provides insight into the model’s expected behavior.

As burnouts grow increasingly common, the necessity for a model describing burnout dynamics becomes increasingly apparent. The model discussed in this paper builds on previous research by adding dreams, a component that has been shown to have an adaptive regulating effect on emotions. The proposed model is a first-order adaptive temporal-causal network model, incorporating emotions, exercise, sleep, and dreams. The model was validated against given patterns found in empirical literature and it may be used to gain a better understanding of burnout dynamics.

In this paper, the effect of a mindfulness therapy based on a Network-Oriented Modeling approach is addressed. The considered therapy is Autogenic Training that can be used when under stress; it has as two main goals to achieve feeling heavy and warm body parts (limbs). Mantras have been used in therapies since long ago to make stressed individuals more relaxed, and they are also used in Autogenic Training. The presented cognitive temporal-causal network model addresses the modeling of Autogenic Training asking this into account. In the first phase a strong stress-inducing stimulus causes the individual to develop an extreme stressful emotion. In the second phase, the therapy with the two goals is shown to make the stressed individual relaxed. Hebbian learning is used to increase the influence of the therapy.

Plasticity is a crucial adaptive characteristic of the brain. Relatively recently mechanisms have been found showing that plasticity itself is controlled by what is called metaplasticity. In this paper a modeling environment is introduced to develop and simulate reified temporal-causal network models that can be applied for cognitive agent models. It is shown how this environment is a useful tool to model plasticity combined with metaplasticity.

In this paper, an adaptive cognitive temporal-causal model using psilocybin for a reduction in extreme emotion is presented. Extreme emotion has an effect on some brain components such as visual cortex, auditory cortex, gustatory cortex, and somatosensory cortex as well as motor cortex such as primary motor cortex, and premotor cortex. Neuroscientific literature reviews show that using psilocybin has a significant effect mostly on two brain components, cerebral cortex, and thalamus. Network-oriented modeling via temporal-causal network-oriented modeling is presented to show the influences of using psilocybin on the cognitive part of the body, same as the brain components. Hebbian learning used to show the adaptivity and learning section of the presented model.

Social media is one of the widely used channels for interpersonal communication, to express feelings and thoughts through certain feedback. Blogs or ecommerce websites share plenty of such information, which serves as a valuable asset, and is also used to make predictions. However, negative feedback can ruin the essence of such platforms, causing frustration among peers. This paper presents a computational network model of a humanoid agent for getting inappropriate feed-backs, who learns to react with a level of competence over aggression due to feedback. Tuning and evaluation of the model is done by performing simulation experiments based on public tweets and mathematical analysis respectively. This model can serve as an input to detect and handle aggression.

It is a proven fact in social sciences that desires for food intake can occur as a result of negative emotions. On the other hand, a negative emotion like anxiety also brings along psychological health issues. In such a situation it’s quite a feasible option to get rid of the worse before the bad. In this paper, a cognitive agent model for food desire regulation is presented wherein Hebbian learning helps in breaking the bond between anxiety/stress and desire for food intake as a result. Simulation results of the model illustrate the food desire and its regulation.

In this paper it is shown how a network can be extended by adding explicit states representing the characteristics defining the structure of the network. Adding such states provides what is called network reification, which can be used to explitly represent network adaptation principles. But also the adaptivity principles may change, based on second-order adaptivity of the network. By another reification step applied to the reified network, also the characteristics of such second-order adaptivity are explicitly represented, thus obtaining multilevel network reification. The construction of multilevel reification introduced here enables representation within a network not only of first-order adaptation principles, but also of second-order adaptation principles expressing change of first-order adaptation principles. The approach is illustrated for an adaptive Social Network involving an adaptive adaptation principle from Social Science based on homophily, by which connections are changing by a first-order adaptation principle which changes over time by a second-order adaptation principle.

In this paper it is analysed how emerging behaviour of an adaptive network can be related to characteristics of the adaptive network’s structure, which includes the structure of the adaptation principles incorporated. In particular, this is addressed for adaptive social networks based on homophily. To this end relevant properties of the network and the adaptation principle have been identified, such as a tipping point for homophily. As one of the results it has been found how the emergence of clusters strongly depends on the value of this tipping point. Moreover, it is shown that some properties of the structure of the network and the adaptation principle entail that the connection weights all converge to 0 (for states in different clusters) or 1 (for states within a cluster).

In this paper, a multi-agent simulation model is presented to investigate the dynamics of ‘mutual-support networks’: online social networks consisting of both humans and artificial agents. Via such networks, human users who are coping with stress can share their problems, via text messages, with human peers as well as ‘artificial friends’. Even though not everybody feels comfortable sharing personal problems with artificial agents, a bot is always available to help human users, and does not face any negative consequence of providing help to stressed peers. Using the simulation model, the dynamics of social networks consisting of an arbitrary combination of humans and agents have been explored under various circumstances. This exploration resulted in several insights that are useful for shaping our vision on artificial friends: (1) humans can provide less emotional support than artificial agents because they have limited emotional resources, (2) the type of support that is provided has a large impact on the human’s stress level, and (3) the more open users are to receiving automated support, the more effective the support is in reducing their stress level. The model was internally validated by means of a mathematical verification.

A new, formal, role-based, framework for modeling and analyzing both real world and artificial organizations is introduced. It exploits static and dynamic properties of the organizational model and includes the (frequently ignored) environment. The transition is described from a generic framework of an organization to its deployed model and to the actual agent allocation. For verification and validation purposes, a set of dedicated techniques is introduced. Moreover, where most models can handle only two or three layered organizational structures, our framework can handle any arbitrary number of organizational layers. Henceforth, real-world organizations can be modeled and analyzed, as illustrated by a case study, within the DEAL project line.

This paper considers the formation control of multiple underactuated surface vessel. A distributed cooperative control using the relative information among neighboring vehicles is proposed such that the flock of multiple vehicles forms a desired geometric formation pattern whose center moves along a desired trajectory. In order to guarantee safe flock navigation and interaction of vehicles with the environment, we propose to extend the designed formation tracking controller to more sophisticated algorithm that prevent the vehicles from colliding with environmental obstacles with unknown sizes and locations based on a Decentralized Constrained Optimizing Problem ”DCOP” strategy. Fil: Rozenfeld, Alejandro Fabian. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Universidad de Las Islas Baleares. Departamento de Fisica; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina Fil: Ghommam, Jawhar. No especifíca; Fil: Picos, Rodrigo. Universidad de Las Islas Baleares. Departamento de Fisica; España Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Universidad de Las Islas Baleares. Departamento de Fisica; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina