History matters: on the mystifying appeal of Bowles and Gintis

Sam Bowles and Herb Gintis have made a broad and sustained contribution to many areas of contemporary economic thought and policy discussions, centring on human interactions in economic settings. Since the mid-1980s, their work, collectively and individually, has developed from a concern with contested exchanges to analyses of behavioural repertoires pursued through evolutionary game theory in which they claim that ‘history matters’. Despite their alignment with the mainstream, they retain an appeal to some heterodox economists. We argue that this appeal is misplaced. Their theoretical work and knowledge claims rest on methodological individualism and equilibrium reasoning, which fosters an obtuse reductionism. They present a confused methodology, which seems to be motivated by a desire to remain coherent to standard economics. We show how their acceptance of methodological individualism and ergodic modelling undermines their knowledge claims as well as their declaration that history matters in their analysis

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements

Optimal and Approximate Q-value Functions for Decentralized POMDPs

Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In single-agent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Q-value functions: an optimal Q-value function Q* is computed in a recursive manner by dynamic programming, and then an optimal policy is extracted from Q*. In this paper we study whether similar Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs), and how policies can be extracted from such value functions. We define two forms of the optimal Q-value function for Dec-POMDPs: one that gives a normative description as the Q-value function of an optimal pure joint policy and another one that is sequentially rational and thus gives a recipe for computation. This computation, however, is infeasible for all but the smallest problems. Therefore, we analyze various approximate Q-value functions that allow for efficient computation. We describe how they relate, and we prove that they all provide an upper bound to the optimal Q-value function Q*. Finally, unifying some previous approaches for solving Dec-POMDPs, we describe a family of algorithms for extracting policies from such Q-value functions, and perform an experimental evaluation on existing test problems, including a new firefighting benchmark problem

Knowing the unknowns: financial policymaking in uncertainty

How do policymakers make decisions during financial market uncertainty? I develop a straightforward framework of policymaking in uncertainty. To overcome uncertainty, policymakers gather information using strategies discussed across a variety of political science disciplines. Policymakers need information to be able to make goal-oriented decisions. The information strategies actors choose are conditioned on the uncertainty problems they face. In turn, the information they receive impacts their policy decisions. My three empirical papers investigate what strategies are likely to be chosen in different types of uncertainty and how these choices affect policy decisions. My first paper, co-written with Mícheál O’Keeffe, develops a signaling game that policymakers play when they perceive data uncertainty, i.e. uncertainty about economic fundamentals. The model is supported empirically with analytic narratives of recent crises in Korea and Ireland. My following two papers deal with situations of increasing causal uncertainty, i.e. uncertainty about how actions cause outcomes. In both of these papers I use Multi-state Event History Analysis. I find that when there is high causal uncertainty policymakers tend to use learning strategies that start with international-level policy recommendations. These recommendations are then updated with the experiences of regional peers who have adopted them. Beyond creating and finding evidence for a parsimonious framework of decisionmaking in uncertainty, I make a number of other contributions to political economy. I extend the empirical tools researchers can use to understand decisions in complex choice environments. I provide evidence that making financial bureaucrats “independent" does not ensure positive outcomes. Specifically, it does not guarantee that financial bureaucrats will provide accurate information needed for effective policymaking

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

Graph dynamics : learning and representation

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 58-60).Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a framework for representation, graphs invite us to simplify and view the world as objects of pure structure whose properties are fixed in time, while the phenomena they are supposed to model are actually often changing. A node alone cannot represent a table leg, for example, because a table leg is not one structure (it can have many different shapes, colors, or it can be seen in many different settings, lighting conditions, etc.) Theories of knowledge representation have in general concentrated on the stability of symbols - on the fact that people often use properties that remain unchanged across different contexts to represent an object (in vision, these properties are called invariants). However, on closer inspection, objects are variable as well as stable. How are we to understand such problems? How is that assembling a large collection of changing components into a system results in something that is an altogether stable collection of parts?(cont.) The work here presents one approach that we came to encompass by the phrase "graph dynamics". Roughly speaking, dynamical systems are systems with states that evolve over time according to some lawful "motion". In graph dynamics, states are graphical structures, corresponding to different hypothesis for representation, and motion is the correction or repair of an antecedent structure. The adapted structure is an end product on a path of test and repair. In this way, a graph is not an exact record of the environment but a malleable construct that is gradually tightened to fit the form it is to reproduce. In particular, we explore the concept of attractors for the graph dynamical system. In dynamical systems theory, attractor states are states into which the system settles with the passage of time, and in graph dynamics they correspond to graphical states with many repairs (states that can cope with many different contingencies). In parallel with introducing the basic mathematical framework for graph dynamics, we define a game for its control, its attractor states and a method to find the attractors. From these insights, we work out two new algorithms, one for Bayesian network discovery and one for active learning, which in combination we use to undertake the object recognition problem in computer vision. To conclude, we report competitive results in standard and custom-made object recognition datasets.by Andre Figueiredo Ribeiro.S.M