Information Diffusion in a Cobweb World

Based on an assumption of one-way learning, Granato and Wong (2004) consider a framework with two groups of agents, Group L and Group H, where Group L is less attentive and uses the expectations of the more or highly attentive Group H to update their forecasts. The paper shows the boomerang effect, which is defined as a situation where the inaccurate forecasts of a less attentive group confound a more attentive group\u27s forecasts. This extended paper relaxes the one-way learning assumption and investigates the case that both groups are learning from each other, i.e., dual learning. Simulations suggest that a boomerang effect still exists. Surprisingly, although the highly attentive group has a full set of information to make forecasts, they still learn from Group L. The reason is that Group H adjusts their forecasts because there is available information in Group L\u27s forecast measurement error

Robust Agent Teams via Socially-Attentive Monitoring

Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications

CAR-Net: Clairvoyant Attentive Recurrent Network

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents' trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents' behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize to unseen scenes.Comment: The 2nd and 3rd authors contributed equall

The Absent-Minded Consumer

We present evidence that many households have only a vague notion of what they are spending on various consumption items. We then develop a life-cycle model that captures this absent-mindedness'. The model generates precautionary spending, whereby absent-minded agents tend to consume more than attentive ones. The model also predicts fluctuations over time in the level of attention, and thereby sheds new light on the sharp reduction in consumption both at retirement, and in cyclical downturns. Finally, we find patterns of attention in the data that are consistent with those predicted by the model.

Crossmodal Attentive Skill Learner

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.Comment: International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2018, NIPS 2017 Deep Reinforcement Learning Symposiu

Foreign stock holdings: the role of information

The household finance literature documents a large fraction of the population not participating in stock markets. It is also puzzling that a much greater share of households do not participate in foreign stock markets. Recent empirical evidence points towards the role of information in determining agents' portfolio choices. I test these results into a model that incorporates information on agents' portfolio allocation decision. In the model, consumers can invest in both domestic and foreign stocks and to update their information set, agents have to pay a cost implying that consumers update their portfolio only infrequently. In addition, to account for the initial costs of acquiring information about stock investments, a version of the model also features an entry-cost to be paid at the first period by agents that decide to enter stock market. Agents that invest in foreign stocks are more attentive, updating their portfolio more frequently. After calibrating the model to match returns and volatility for the U.S. economy and di¤erent foreign stock investments, I obtain that the minimum entry cost necessary to drive households completely out of stock markets is large (and in line with the equity premium puzzle literature). However, once agents already invest in domestic stock markets, the minimum cost that would drive investors out of foreign stocks market is much smaller. The size of the latter minimum entry cost depends on model parameters assumptions, and small variations on risk aversion and uncertaintly about foreign asset returns can bring this entry cost down enough to justify the substancial non-participation in foreign stock markets.Stockholders ; Stock market