Interacting Unities: An Agent-Based System

Recently architects have been inspired by Thompsonis Cartesian deformations and Waddingtonis flexible topological surface to work within a dynamic field characterized by forces. In this more active space of interactions, movement is the medium through which form evolves. This paper explores the interaction between pedestrians and their environment by regarding it as a process occurring between the two. It is hypothesized that the recurrent interaction between pedestrians and environment can lead to a structural coupling between those elements. Every time a change occurs in each one of them, as an expression of its own structural dynamics, it triggers changes to the other one. An agent-based system has been developed in order to explore that interaction, where the two interacting elements, agents (pedestrians) and environment, are autonomous units with a set of internal rules. The result is a landscape where each agent locally modifies its environment that in turn affects its movement, while the other agents respond to the new environment at a later time, indicating that the phenomenon of stigmergy is possible to take place among interactions with human analogy. It is found that it is the environmentis internal rules that determine the nature and extent of change

One, two, (three), infinity: Newspaper and lab beauty-contest experiments

"Beauty-contest" is a game in which participants have to choose, typically, a number in [0,100], the winner being the person whose number is closest to a proportion of the average of all chosen numbers. We describe and analyze Beauty-contest experiments run in newspapers in UK, Spain, and Germany and find stable patterns of behavior across them, despite the uncontrollability of these experiments. These results are then compared with lab experiments involving undergraduates and game theorists as subjects, in what must be one of the largest empirical corroborations of interactive behavior ever tried. We claim that all observed behavior, across a wide variety of treatments and subject pools, can be interpreted as iterative reasoning. Level-1 reasoning, Level-2 reasoning and Level-3 reasoning are commonly observed in all the samples, while the equilibrium choice (Level-Maximum reasoning) is only prominently chosen by newspaper readers and theorists. The results show the empirical power of experiments run with large subject-pools, and open the door for more experimental work performed on the rich platform offered by newspapers and magazines.Experiments, bounded rationality, Beauty-contest, parallelism, Leex

Tantalizing dilaton tests from a near-conformal EFT

The dilaton low-energy effective field theory (EFT) of an emergent light scalar is probed in the paradigm of strongly coupled near-conformal gauge theories. These studies are motivated by models which exhibit small $\beta$-functions near the conformal window (CW), perhaps with slow scale-dependent walking and a light scalar with ${ 0^{++} }$ quantum numbers. We report our results from the hypothesis of a dilaton inspired EFT analysis with two massless fermions in the two-index symmetric (sextet) representation of the SU(3) color gauge group. With important caveats in our conclusions, conformal symmetry breaking entangled with chiral symmetry breaking would drive the near-conformal infrared behavior of the theory predicting characteristic dilaton signatures of the light scalar from broken scale invariance when probed on relevant scales of fermion mass deformations. From a recently reasoned choice of the dilaton potential in the EFT description~\cite{Golterman:2016lsd} we find an unexpectedly light dilaton mass in the chiral limit at $m_d/f_\pi = 1.56(28)$, set in units of the pion decay constant $f_\pi$. Subject to further statistical and systematic tests of continued post-conference analysis, this result is significantly lower than our earlier estimates from less controlled extrapolations of the light scalar (the $\sigma$-particle) to the massless fermion limit of chiral perturbation theory. We also discuss important distinctions between the dilaton EFT analysis and the linear $\sigma$-model without dilaton signatures. For comparative reasons, we comment on dilaton tests from recent work with fermions in the fundamental representation with $n_f=8$ flavors.Comment: 14 pages, 34 figures, Proceedings of the 36th International Symposium on Lattice Field Theory (Lattice 2018), July 22-28, 2018, East Lancing, USA; elimination of some fit redundancies with minor changes in related figure

The Hopfield model and its role in the development of synthetic biology

Neural network models make extensive use of concepts coming from physics and engineering. How do scientists justify the use of these concepts in the representation of biological systems? How is evidence for or against the use of these concepts produced in the application and manipulation of the models? It will be shown in this article that neural network models are evaluated differently depending on the scientific context and its modeling practice. In the case of the Hopfield model, the different modeling practices related to theoretical physics and neurobiology played a central role for how the model was received and used in the different scientific communities. In theoretical physics, where the Hopfield model has its roots, mathematical modeling is much more common and established than in neurobiology which is strongly experiment driven. These differences in modeling practice contributed to the development of the new field of synthetic biology which introduced a third type of model which combines mathematical modeling and experimenting on biological systems and by doing so mediates between the different modeling practices

Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction

This paper introduces a novel neural network-based reinforcement learning approach for robot gaze control. Our approach enables a robot to learn and to adapt its gaze control strategy for human-robot interaction neither with the use of external sensors nor with human supervision. The robot learns to focus its attention onto groups of people from its own audio-visual experiences, independently of the number of people, of their positions and of their physical appearances. In particular, we use a recurrent neural network architecture in combination with Q-learning to find an optimal action-selection policy; we pre-train the network using a simulated environment that mimics realistic scenarios that involve speaking/silent participants, thus avoiding the need of tedious sessions of a robot interacting with people. Our experimental evaluation suggests that the proposed method is robust against parameter estimation, i.e. the parameter values yielded by the method do not have a decisive impact on the performance. The best results are obtained when both audio and visual information is jointly used. Experiments with the Nao robot indicate that our framework is a step forward towards the autonomous learning of socially acceptable gaze behavior.Comment: Paper submitted to Pattern Recognition Letter

String rewriting for Double Coset Systems

In this paper we show how string rewriting methods can be applied to give a new method of computing double cosets. Previous methods for double cosets were enumerative and thus restricted to finite examples. Our rewriting methods do not suffer this restriction and we present some examples of infinite double coset systems which can now easily be solved using our approach. Even when both enumerative and rewriting techniques are present, our rewriting methods will be competitive because they i) do not require the preliminary calculation of cosets; and ii) as with single coset problems, there are many examples for which rewriting is more effective than enumeration. Automata provide the means for identifying expressions for normal forms in infinite situations and we show how they may be constructed in this setting. Further, related results on logged string rewriting for monoid presentations are exploited to show how witnesses for the computations can be provided and how information about the subgroups and the relations between them can be extracted. Finally, we discuss how the double coset problem is a special case of the problem of computing induced actions of categories which demonstrates that our rewriting methods are applicable to a much wider class of problems than just the double coset problem.Comment: accepted for publication by the Journal of Symbolic Computatio

Experimentation in Two-Sided Markets

We study optimal experimentation by a monopolistic platform in a two-sided market framework. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time infinite-horizon framework by setting participation fees or quantities on both sides. We show that a price-setting platform provider sets a fee lower than the myopically optimal level on at least one side of the market, and on both sides if the two externalities are of approximately equal strenght. If the externality that one side exerts is sufficiently weaker than the externality it experiences, the optimal fee on this side exceeds the myopically optimal level. We obtain analogous results for expected prives when the platform provider chooses quantities. While the optimal policy does not admin closed-form representations in general, we identify special cases in which the undiscounted limit of the model can be solved in closed form