A simple axiomatics of dynamic play in repeated games

This paper proposes an axiomatic approach to study two-player infinitely repeated games. A solution is a correspondence that maps the set of stage games into the set of infinite sequences of action profiles. We suggest that a solution should satisfy two simple axioms: individual rationality and collective intelligence. The paper has three main results. First, we provide a classification of all repeated games into families, based on the strength of the requirement imposed by the axiom of collective intelligence. Second, we characterize our solution as well as the solution payoffs in all repeated games. We illustrate our characterizations on several games for which we compare our solution payoffs to the equilibrium payoff set of Abreu and Rubinstein (1988). At last, we develop two models of players' behavior that satisfy our axioms. The first model is a refinement of subgame-perfection, known as renegotiation proofness, and the second is an aspiration-based learning model.Axiomatic approach, repeated games, classification of games, learning, renegotiation

Sharing and Co?generating Knowledges: Reflections on Experiences with PRA and CLTS

The evolution and spread of PRA (Participatory Rural Appraisal or Participatory Reflection and Action) and CLTS (Community?Led Total Sanitation) have involved activities of sharing and co?generating knowledge which can loosely be considered a form of Action Learning. Key activities for this have been sequences of participatory workshops which have evolved as creative collective experiences fed by and feeding into wider networking and dissemination. These workshops have been occasions for sharing practice and collating experiences, and going beyond these to generate ideas and evolve and agree principles and good practices. Critical reflections concern power, planning and process, theory of change and impact, lessons learnt, and an ongoing learning process

Framing Situated Professional Knowledge in Online Learning Communities

This paper deepens the theoretical understanding that underpins collaboration through social interaction in professional online learning environments. It explores the use of framing as a theoretical lens to assess situated learning in online graduate education. We explore how collaborative knowledge construction is framed in an intense 10 week graduate IS Project Management course. We present a taxonomy of frame challenging, problematization, and legitimation to demonstrate how individual and collective forms of knowledge construction contribute to group learning about professional practice in the context of action. We close with a model that demonstrates how community knowledge is co-constructed through sequences of contextualized frame-proposal, reflective comparison with own experience, frame-problematization and debate, and generic-legitimation of a consensus frame

Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition

We present a unified framework for understanding human social behaviors in raw image sequences. Our model jointly detects multiple individuals, infers their social actions, and estimates the collective actions with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end to generate dense proposal maps that are refined via a novel inference scheme. The temporal consistency is handled via a person-level matching Recurrent Neural Network. The complete model takes as input a sequence of frames and outputs detections along with the estimates of individual actions and collective activities. We demonstrate state-of-the-art performance of our algorithm on multiple publicly available benchmarks

Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

Prospects of reinforcement learning for the simultaneous damping of many mechanical modes

We apply adaptive feedback for the partial refrigeration of a mechanical resonator, i.e. with the aim to simultaneously cool the classical thermal motion of more than one vibrational degree of freedom. The feedback is obtained from a neural network parametrized policy trained via a reinforcement learning strategy to choose the correct sequence of actions from a finite set in order to simultaneously reduce the energy of many modes of vibration. The actions are realized either as optical modulations of the spring constants in the so-called quadratic optomechanical coupling regime or as radiation pressure induced momentum kicks in the linear coupling regime. As a proof of principle we numerically illustrate efficient simultaneous cooling of four independent modes with an overall strong reduction of the total system temperature.Comment: Machine learning in Optomechanics: coolin