Michael Harré

Phase-transition–like behaviour of information measures in financial markets

We apply measures based on information theory to the analysis of day close equity prices traded on US stock markets over the 13-year interval from 1995 up until after the market crash of September 2008. We show that the mutual information between prices provides insight into the changing relationships between equities over a time period which includes three known market crashes and two events which have not previously been included in this type of study, one of which is related to the sub-prime meltdown starting in 2007. Specifically, the mutual information around market crashes shows behaviour typical of the phase transitions studied in condensed-matter physics, however similar but more extended peaks in mutual information are also observed at other times not associated with any known market crashes.

Information and phase transitions in socio-economic systems

We examine the role of information-based measures in detecting and analysing phase transitions. We contend that phase transitions have a general character, visible in transitions in systems as diverse as classical flocking models, human expertise, and social networks. Information-based measures such as mutual information and transfer entropy are particularly suited to detecting the change in scale and range of coupling in systems that herald a phase transition in progress, but their use is not necessarily straightforward, possessing difficulties in accurate estimation due to limited sample sizes and the complexities of analysing non-stationary time series. These difficulties are surmountable with careful experimental choices. Their effectiveness in revealing unexpected connections between diverse systems makes them a promising tool for future research.

A semantic network approach to the Creativity Quotient (CQ)

The Creativity Quotient (CQ) is a novel metric building on ideational fluency that accounts for both the number of novel ideas (ideation) and the number of distinct categories (fluency) these ideas fall into. Categories are, however, difficult to define unambiguously and objectively. We propose that the principal contribution of this article is an entirely algorithmic approach based on concept networks, and an information metric defined thereon. It requires only measures of the similarity between concepts, which may come from databases such as Wordnet, Wikipedia, Google, or corpus analysis tools. In the special case of strong, unique categories it reduces directly to CQ.

Strategic Choice of Preferences: The Persona Model

Recent work in several fields has established that humans can adopt binding “behavioral” preferences and convincingly signal those preferences to other humans, either via their behavior or via their body language / tone of voice. In this paper, we model the strategic implications of this ability. Our thesis is that through a persons lifetime they (perhaps subconsciously) learn what such signaled, binding behavioral preferences result in the highest value of their actual preferences, given the resultant behavior of other players. We argue that this “persona” model may explain why many interpersonal preferences have the particular form they do. As an illustration, we use the persona model to explain cooperation in non-repeated versions of the Prisoners Dilemma (PD). We also provide quantitative predictions to distinguish this explanation of cooperation from simply assuming people have actual preferences biased towards cooperation. In particular, we show that the persona model predicts a “crowding out” phenomenon in the PD, in which introducing incentives to cooperate causes players to stop cooperating instead. We also use the persona model to predict a tradeoff between the robustness of cooperation in the PD and the benefit of that cooperation.

The Development of Human Expertise in a Complex Environment

We introduce an innovative technique that quantifies human expertise development in such a way that humans and artificial systems can be directly compared. Using this technique we are able to highlight certain fundamental difficulties associated with the learning of a complex task that humans are still exceptionally better at than their computer counterparts. We demonstrate that expertise goes through significant developmental transitions that have previously been predicted but never explicated. The first signals the onset of a steady increase in global awareness that begins surprisingly late in expertise acquisition. The second transition, reached by only a very few experts in the world, shows a major reorganisation of global contextual knowledge resulting in a relatively minor gain in skill. We are able to show that these empirical findings have consequences for our understanding of the way in which expertise acquisition may be modelled by learning in artificial intelligence systems. This point is emphasised with a novel theoretical result showing explicitly how our findings imply a non-trivial hurdle for learning for suitably complex tasks.

Utility, Revealed Preferences Theory, and Strategic Ambiguity in Iterated Games

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date, very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent’s next action by introducing two new classes of games: “invertible games” and “cyclical games”. By explicitly expanding out the mutual information between past states and the next action we show under what circumstances the explicit values of the utility are irrelevant for iterated games and this is then related to revealed preferences theory of classical economics. These information measures are then applied to the Traveler’s Dilemma game and the Prisoner’s Dilemma game, the Prisoner’s Dilemma being invertible, to illustrate their use. In the Prisoner’s Dilemma, a novel connection is made between the computational principles of logic gates and both the structure of games and the agents’ decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: “Tit-for-Tat” and “Win-Stay, Lose-Switch”.

Intuitive Expertise and Perceptual Templates

We provide the first demonstration of an artificial neural network encoding the perceptual templates that form an important component of the high level strategic understanding developed by experts. Experts have a highly refined sense of knowing where to look, what information is important and what information to ignore. The conclusions these experts reach are of a higher quality and typically made in a shorter amount of time than those of non-experts. Understanding the manifestation of such abilities in terms of both the psychology of expert performance and the underlying neural mechanisms constitutes one of the most challenging problems in the cognitive sciences. Using perceptual templates we show how the amount of contextual information can change significantly even within a given task, the relationship between local and non-local contexts and finally why there is very little correlation between measures of intelligence and level of expertise in many of the most complex tasks performed by humans.

Stochastic Reinforcement in Evolutionary Multi-Agent Game Playing of Dots-and-Boxes

An evolutionary multi-agent system is described that develops a rule-based approach to playing the game Dots and Boxes, under a probabilistic reinforcement learning paradigm. The process and behaviour using probabilistic action selection with a Boltzmann distribution is compared with an alternative technique using an Artificial Economy. The probabilistic system developed was played against a rule-based software opponent, and able to produce behaviour under a self-organising process able to perform better than the software opponent it was trained against.

Strategic Islands in Economic Games: Isolating Economies From Better Outcomes

Many of the issues we face as a society are made more problematic by the rapidly changing context in which important decisions are made. For example buying a petrol powered car is most advantageous when there are many petrol pumps providing cheap petrol whereas buying an electric car is most advantageous when there are many electrical recharge points or high capacity batteries available. Such collective decision-making is often studied using economic game theory where the focus is on how individuals might reach an agreement regarding the supply and demand for the different energy types. But even if the two parties find a mutually agreeable strategy, as technology and costs change over time, for example through cheaper and more efficient batteries and a more accurate pricing of the total cost of oil consumption, so too do the incentives for the choices buyers and sellers make, the result of which can be the stranding of an industry or even a whole economy on an island of inefficient outcomes. In this article we consider the issue of how changes in the underlying incentives can move us from an optimal economy to a sub-optimal economy while at the same time making it impossible to collectively navigate our way to a better strategy without forcing us to pass through a socially undesirable “tipping point”. We show that different perturbations to underlying incentives results in the creation or destruction of “strategic islands” isolated by disruptive transitions between strategies. The significant result in this work is the illustration that an economy that remains strategically stationary can over time become stranded in a suboptimal outcome from which there is no easy way to put the economy on a path to better outcomes without going through an economic tipping point.

Fisher Transfer Entropy: Quantifying the gain in transient sensitivity

We introduce a novel measure, Fisher transfer entropy (FTE), which quantifies a gain in sensitivity to a control parameter of a state transition, in the context of another observable source. The new measure captures both transient and contextual qualities of transfer entropy and the sensitivity characteristics of Fisher information. FTE is exemplified for a ferromagnetic two-dimensional lattice Ising model with Glauber dynamics and is shown to diverge at the critical point.