A mathematical model was developed for opinion propagation on online social networks using a scale-free network with an adjustable clustering coefficient. Connected nodes influence each other when the difference between their opinion values is less than a threshold value. The model is used to examine effectiveness of three different approaches for influencing public opinion. The approaches examined include (1) a "Class", defined as an approach (such as a class or book) that greatly influences a small, randomly selected portion of the population, (2) an "Advertisement", defined as an approach (such as a TV or online advertisement) that has a small influence at each viewing on a large randomly selected portion of the population, and (3) an "App", defined as an approach (such as a Facebook game or smartphone "App") that spreads via the online social network (rather than randomly) and has a small influence at each viewing on the affected population. The Class and Advertisement approaches result in similar overall influence on the population, despite the fact that these approaches are highly different. In contrast, the App approach has a much more significant effect on opinion values of users occupying clusters within the social network compared to the overall population.

The evolution of unconditional cooperation is one of the fundamental problems in science. A new solution is proposed to solve this puzzle. We treat this issue with an evolutionary model in which agents play the Prisoner's Dilemma on signed networks. The topology is allowed to co-evolve with relational signs as well as with agent strategies. We introduce a strategy that is conditional on the emotional content embedded in network signs. We show that this strategy acts as a catalyst and creates favorable conditions for the spread of unconditional cooperation. In line with the literature, we found evidence that the evolution of cooperation most likely occurs in networks with relatively high chances of rewiring and with low likelihood of strategy adoption. While a low likelihood of rewiring enhances cooperation, a very high likelihood seems to limit its diffusion. Furthermore, unlike in nonsigned networks, cooperation becomes more prevalent in denser topologies.

In this paper, we report on the theoretical foundations, empirical context and technical implementation of an agent-based modeling (ABM) framework, that uses a high-performance computing (HPC) approach to investigate human population dynamics on a global scale, and on evolutionary time scales. The ABM-HPC framework provides an in silico testbed to explore how short-term/small-scale patterns of individual human behavior and long-term/large-scale patterns of environmental change act together to influence human dispersal, survival and extinction scenarios. These topics are currently at the center of the Neanderthal debate, i.e., the question why Neanderthals died out during the Late Pleistocene, while modern humans dispersed over the entire globe. To tackle this and similar questions, simulations typically adopt one of two opposing approaches, top-down (equation-based) and bottom-up (agent-based) models of population dynamics. We propose HPC technology as an essential computational tool to bridge the gap between these approaches. Using the numerical simulation of worldwide human dispersals as an example, we show that integrating different levels of model hierarchy into an ABM-HPC simulation framework provides new insights into emergent properties of the model, and into the potential and limitations of agent-based versus continuum models.

Massively distributed data mining in large networks such as smart device platforms and peer-to-peer systems is a rapidly developing research area. One important problem here is concept drift, where global data patterns (movement, preferences, activities, etc.) change according to the actual set of participating users, the weather, the time of day, or as a result of events such as accidents or even natural catastrophes. In an important case — when the network is very large but only a few training samples can be obtained at each node locally — no efficient distributed solution is known that could follow concept drift efficiently. This case is characteristic of smart device platforms where each device stores only one local observation or data record related to a learning problem. Here we present two algorithms to handle concept drift. None of the algorithms collects data to a central location, instead models of the data perform random walks in the network, while being improved using an online learning algorithm. The first algorithm achieves adaptivity by maintaining young as well as old models in the network according to a fixed age distribution. The second one measures the performance of models locally, and discards them if they are judged outdated. We demonstrate through a thorough experimental analysis that our algorithms outperform the known competing methods if the number of independent local samples is limited relative to the speed of drift: a typical scenario in our targeted application domains. The two algorithms have different strengths: while the age distribution approach is very simple and efficient, explicit drift detection can be useful in monitoring applications to trigger control action.

Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the self-organization of behavior, e.g., to increase fault tolerance and enhance flexibility, provided that external goals can also be achieved. We present results on the guidance of self-organizing control by visual target stimuli and show a remarkable robustness to sensorimotor disruptions. In a proof of concept study an autonomous wheeled robot is learning an object finding and ball-pushing task from scratch within a few minutes in continuous domains. The robustness is demonstrated by the rapid recovery of the performance after severe changes of the sensor configuration.

We study goal-directed behavior in the light of informationally constrained cognition. In a formal information-theoretical model, based on a description of goal-directed behavior as a family of Markov Decision Processes, we study lower bounds of constraints on the information about a goal needed to generate behavior that achieves such a goal at a certain level of optimality. We assume a working memory that operates on this minimally relevant goal information and study the necessary dynamics of in and out flow of information for such a working memory. Finally, we formally analyze explicit constraints on goal information pathways as information bottlenecks. Our results show that intrinsic and behavioral organizations, such as ritualized behavior, salient sub-goals, and natural abstractions, appear as a result of the studied informational constraints. We argue that a closed approach to generate a family of organizational concepts in a coherent way by systematically applying cognitive constraints as taken in this work can constitute an important step toward guiding self-organization.

We comment on the derivation of the main equation in the bounded confidence model of opinion dynamics. In the original work, the equation is derived using an ad-hoc counting method. We point that the original derivation does contain some small mistake. The mistake does not have a large qualitative impact, but it reveals the danger of the ad-hoc counting method. We show how a more systematic approach, which we call micro to macro, can avoid such mistakes, without adding any significant complexity.

Can the discussions about new issues challenge the organization of a society? That is, the question we tried to answer with a very simple model considering a society where individuals are organized into groups. An individual discuss its opinion on this new issue following the rule of the bounded confidence model implying that an individual influences and is influenced when it is sufficiently open-minded. One of the main ingredients of the model is the groups that both influence the behavior of the individual and characterize the social network topology. Individuals choose the group membership according to their opinion. Starting from an initial homogeneous group size distribution, four different prototypical regimes can be reproduced by the model, depending on the individual open-mindedness: From a high heterogeneity condition, with one or two largely major groups, to an invariant situation of homogeneity. The heterogeneity is explained by the dynamics of individuals. In this paper, we extend the studies of a previous work on the argument, deepening the analysis on the internal structure of the groups and on the dynamical process itself. To do this, we analyze indicators related to the stability of the obtained virtual society. We found that the dynamics of groups is not stable when open-mindedness is small, even if the dynamics of opinions reaches equilibrium. On the other side, for larger values of the open-mindedness, both the opinion and the group dynamics stabilize. Thus, our study shows that the introduction of a new topic of discussion in a society can strongly challenge group structures, both concerning their stability and their size.

This paper applies Bayesian estimation to an open-economy Dynamic Stochastic General Equilibrium (DSGE) model of Japan, to assess the effects of expanding government debt on interest rates, real exchange rate dynamics, and real sector performance. We find that the emergence of even a small risk premium on government debt will trigger considerable instability in the real and nominal variables. We show that a switch to an exchange-rate rule for monetary policy would considerably moderate the instability induced by a rising risk premium.

Since the start of the financial crisis in 2007, the debate on the proper level leverage of financial institutions has been flourishing. The paper addresses such crucial issue within the Eurace artificial economy, by considering the effects that different choices of capital adequacy ratios for banks have on main economic indicators. The study also gives us the opportunity to examine the outcomes of the Eurace model so to discuss the nature of endogenous money, giving a contribution to a debate that has grown stronger over the last two decades. A set of 40 years long simulations have been performed and examined in the short (first five years), medium (the following 15 years) and long (the last 20 years) run. Results point out a non-trivial dependence of real economic variables such as the gross domestic product (GDP), the unemployment rate and the aggregate capital stock on banks' capital adequacy ratios; this dependence is in place due to the credit channel and varies significantly according to the chosen evaluation horizon. In general, while boosting the economy in the short run, regulations allowing for a high leverage of the banking system tend to be depressing in the medium and long run. Results also point out that the stock of money is driven by the demand for loans, therefore supporting the theory of endogenous nature of credit money.