Measuring autonomy and emergence via Granger causality

Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Politics, Influence, and the Small Scale Organization of Political Communication Networks

This paper addresses the factors that give rise to both heterogeneous and homogeneous opinion distributions within political communication networks. We argue that the factors sustaining homogeneity and heterogeneity are not entirely symmetrical – heterogeneity is not necessarily explained by treating it as the flip side of homogeneity. Two primary questions guide the effort. If influence within a dyad depends on the distribution of opinions beyond the dyad, is dyadic influence contingent on the construction of the network within which the dyad is located? In particular, how does the micro-structure of the larger network affect the persuasiveness of communication within the dyad? We pursue an analysis based on agent based models of the communication process. The analysis points toward the importance of particular forms of small scale organization in preserving homogeneous opinion distributions. Homogeneity is more likely when network density is particularly high – when direct connections are more frequent among more agents. Correspondingly, when we observe homogeneity within communication networks in the natural world, the organization and reach of small scale social organization is likely to be key

Fostering efficiency of computational resource allocation - Integrating information services into markets

The application of market mechanisms for the allocation of computing services is a demanding task, which requires bridging economic and associated technical challenges. Even if the market-based approach promises an efficient allocation of computing services, the wide heterogeneity of consumer requirements and the diversity of computational services on provider side are challenging the processes of finding, allocating, and using an appropriate service in an autonomous way. The focus of the most papers is mainly devoted to the optimization embedded in the allocation process itself. However, we think that the optimization process starts much earlier and contains the information gathering until the final market-based resource allocations. In this paper we introduce an integrated framework for market-based allocation of computing services, integrating information retrieval of market information, prediction models, bidding strategies and marked mechanisms. As proof-of-concept, we implemented a first prototype of the framework. Furthermore, we propose a methodology for evaluating strategic behavior in market mechanisms with bidding strategies using market information and statistical prediction techniques. First simulation results show strategic behavior in selected market mechanisms by applying the proposed techniques

The 2nd Place Solution for 2023 Waymo Open Sim Agents Challenge

In this technical report, we present the 2nd place solution of 2023 Waymo Open Sim Agents Challenge (WOSAC)[4]. We propose a simple yet effective autoregressive method for simulating multi-agent behaviors, which is built upon a well-known multimodal motion forecasting framework called Motion Transformer (MTR)[5] with postprocessing algorithms applied. Our submission named MTR+++ achieves 0.4697 on the Realism Meta metric in 2023 WOSAC. Besides, a modified model based on MTR named MTR_E is proposed after the challenge, which has a better score 0.4911 and is ranked the 3rd on the leaderboard of WOSAC as of June 25, 2023

Financial power laws: Empirical evidence, models, and mechanism

Financial markets (share markets, foreign exchange markets and others) are all characterized by a number of universal power laws. The most prominent example is the ubiquitous finding of a robust, approximately cubic power law characterizing the distribution of large returns. A similarly robust feature is long-range dependence in volatility (i.e., hyperbolic decline of its autocorrelation function). The recent literature adds temporal scaling of trading volume and multi-scaling of higher moments of returns. Increasing awareness of these properties has recently spurred attempts at theoretical explanations of the emergence of these key characteristics form the market process. In principle, different types of dynamic processes could be responsible for these power-laws. Examples to be found in the economics literature include multiplicative stochastic processes as well as dynamic processes with multiple equilibria. Though both types of dynamics are characterized by intermittent behavior which occasionally generates large bursts of activity, they can be based on fundamentally different perceptions of the trading process. The present chapter reviews both the analytical background of the power laws emerging from the above data generating mechanism as well as pertinent models proposed in the economics literature. --