Fund managers - why the best might be the worst: On the evolutionary vigor of risk-seeking behavior

This article explores the influence of competitive conditions on the evolutionary fitness of different risk preferences. As a practical example, the professional competition between fund managers is considered. To explore how different settings of competition parameters, the exclusion rate and the exclusion interval, affect individual investment behavior, an evolutionary model based on a genetic algorithm is developed. The simulation experiments indicate that the influence of competitve conditions on investment behavior and attitudes towards risk is significant. What is alarming is that intense competitive pressure generates riskseeking behavior and undermines the predominance of the most skilled. --risk preferences,competition,genetic programming,fund managers,portfolio theory

Static analysis techniques to verify mutual exclusion situations within SysML models

AVATAR is a real-time extension of SysML supported by the TTool open-source toolkit. So far, formal verification of AVATAR models has relied on reachability techniques that face a state explosion problem. The paper explores a new avenue: applying structural analysis to AVATAR model, so as to identify mutual exclusion situations. In practice, TTool translates a subset of an AVATAR model into a Petri net and solves an equation system built upon the incidence matrix of the net. TTool implements a push-button approach and displays verification results at the AVATAR model level. The approach is not restricted to AVATAR and may be adapted to other UML profiles

A New Framework for Decomposing Multivariate Information

What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much-criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. This thesis presents a new framework for information decomposition that is based upon the decomposition of pointwise mutual information rather than mutual information. The framework is derived in two separate ways. The first of these derivations is based upon a modified version of the original axiomatic approach taken by Williams and Beer. However, to overcome the difficulty associated with signed pointwise mutual information, the decomposition is applied separately to the unsigned entropic components of pointwise mutual information which are referred to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Based upon an operational interpretation of redundancy, measures of redundant specificity and redundant ambiguity are defined which enables one to evaluate the partial information atoms separately for each lattice. These separate atoms can then be recombined to yield the sought-after multivariate information decomposition. This framework is applied to canonical examples from the literature and the results and various properties of the decomposition are discussed. In particular, the pointwise decomposition using specificity and ambiguity is shown to satisfy a chain rule over target variables, which provides new insights into the so-called two-bit-copy example. The second approach begins by considering the distinct ways in which two marginal observers can share their information with the non-observing individual third party. Several novel measures of information content are introduced, namely the union, intersection and unique information contents. Next, the algebraic structure of these new measures of shared marginal information is explored, and it is shown that the structure of shared marginal information is that of a distributive lattice. Furthermore, by using the fundamental theorem of distributive lattices, it is shown that these new measures are isomorphic to a ring of sets. Finally, by combining this structure together with the semi-lattice of joint information, the redundancy lattice form partial information decomposition is found to be embedded within this larger algebraic structure. However, since this structure considers information contents, it is actually equivalent to the specificity lattice from the first derivation of pointwise partial information decomposition. The thesis then closes with a discussion about whether or not one should combine the information contents from the specificity and ambiguity lattices

Detecting Deadlocks in Concurrent Systems

We use a geometric description for deadlocks occurring in schedulingproblems for concurrent systems to construct a partial order and hence a directed graph, in which the local maxima correspond to deadlocks. Algorithms finding deadlocks are described and assessed.Keywords: deadlock, partial order, search algorithm, concurrency, distributedsystems

Detecting Deadlocks in Concurrent Systems

We use a geometric description for deadlocks occurring in schedulingproblems for concurrent systems to construct a partial order and hence a directed graph, in which the local maxima correspond to deadlocks. Algorithms finding deadlocks are described and assessed.Keywords: deadlock, partial order, search algorithm, concurrency, distributedsystems

Introducing a differentiable measure of pointwise shared information

Partial information decomposition (PID) of the multivariate mutual information describes the distinct ways in which a set of source variables contains information about a target variable. The groundbreaking work of Williams and Beer has shown that this decomposition cannot be determined from classic information theory without making additional assumptions, and several candidate measures have been proposed, often drawing on principles from related fields such as decision theory. None of these measures is differentiable with respect to the underlying probability mass function. We here present a novel measure that satisfies this property, emerges solely from information-theoretic principles, and has the form of a local mutual information. We show how the measure can be understood from the perspective of exclusions of probability mass, a principle that is foundational to the original definition of the mutual information by Fano. Since our measure is well-defined for individual realizations of the random variables it lends itself for example to local learning in artificial neural networks. We also show that it has a meaningful M\"{o}bius inversion on a redundancy lattice and obeys a target chain rule. We give an operational interpretation of the measure based on the decisions that an agent should take if given only the shared information.Comment: 19 pages, 6 figures; title modified, text modified, typos corrected, manuscript publishe

Applications of Nonclassical Logic Methods for Purposes of Knowledge Discovery and Data Mining

* The work is partially supported by Grant no. NIP917 of the Ministry of Science and Education – Republic of Bulgaria.Methods for solution of a large class of problems on the base of nonclassical, multiple-valued, and probabilistic logics have been discussed. A theory of knowledge about changing knowledge, of defeasible inference, and network approach to an analogous derivation have been suggested. A method for regularity search, logic-axiomatic and logic-probabilistic methods for learning of terms and pattern recognition in the case of multiple-valued logic have been described and generalized. Defeasible analogical inference and new forms of inference using exclusions are considered. The methods are applicable in a broad range of intelligent systems

Folded Interaction Systems and Their Application to the Survivability Analysis of Unbounded Systems

Modeling the fulfillment of global properties like survivability is a challenging problem in unbounded systems such as Grids, peer-to-peer systems, or swarms. This paper proposes Folded Interaction Systems (FIS), an extension of the classic I-Systems framework, to overcome the modeling issues. FIS is applied to a case of survivability assessment in Grids and demonstrates the identification of essential capabilities, the modeling of harmful incidents, and the derivation of standard strategies to sustain the survival of a system’s mission. FIS is not restricted to survivability, it can be used for investigating the preservation of any global property