We present first results from the use of XGBoost, a highly effective machine learning (ML) method, within the Bristol Betting Exchange (BBE), an open-source agent-based model (ABM) designed to simulate a contemporary sports-betting exchange with in-play betting during track-racing events such as horse races. We use the BBE ABM and its array of minimally-simple bettor-agents as a synthetic data generator which feeds into our XGBoost ML system, with the intention that XGBoost discovers profitable dynamic betting strategies by learning from the more profitable bets made by the BBE bettor-agents. After this XGBoost training, which results in one or more decision trees, a bettor-agent with a betting strategy determined by the XGBoost-learned decision tree(s) is added to the BBE ABM and made to bet on a sequence of races under various conditions and betting-market scenarios, with profitability serving as the primary metric of comparison and evaluation. Our initial findings presented here show that XGBoost trained in this way can indeed learn profitable betting strategies, and can generalise to learn strategies that outperform each of the set of strategies used for creation of the training data. To foster further research and enhancements, the complete version of our extended BBE, including the XGBoost integration, has been made freely available as an open-source release on GitHub.

In 2017 Robert Shiller, a Nobel Laureate, introduced Narrative Economics, an approach to explaining aspects of economies that are difficult to comprehend when analyzed using conventional methods: in light of narratives (i.e., stories) that participants in asset markets hear, believe, and tell each other, some observable economic factors, such as price dynamics of otherwise valueless digital assets, can be explained largely within the context of those narratives. As Shiller argues, it is best to explain and understand seemingly irrational and hard-to-explain behaviors, such as investing in highly volatile cryptocurrency markets, in narrative terms: people invest because they believe that it makes sense to do so, or have a heartfelt opinion about the prospects of the asset, and they share these beliefs and opinions with themselves and others in the form of narratives. In this paper, we address the question of how an agent-based modeling platform can be developed to be used for studying narrative economics. To do this, we integrate two very recently published developments. From the field of agent-based models of financial markets, we use the PRDE adaptive zero-intelligence trader strategy introduced by Cliff (2022), and we extend it to integrate a continuous-time real-valued nonlinear opinion dynamics model reported by Bizyaeva et al. (2022). In our integrated system, each trader holds an opinion variable whose value can be altered by interaction with other agents, modeling the influence that narratives have on an agent’s opinions, and which can also be altered by observation of events in the market. Furthermore, the PRDE algorithm is modified to allow each trader’s trading behavior to smoothly alter as that trader’s opinion dynamically varies. Results reported for the first time here show that in our model there is a tightly coupled circular interplay between opinions and prices: changes in the distribution of opinions can affect subsequent price dynamics; and changes in price dynamics can affect the consequent distribution of opinions. Thus this paper presents a first demonstration of the reliability and effectiveness of our new agent-based modeling platform for use in studying issues in narrative economics. Python source-code for our model is being made freely available as open-source release on GitHub, to allow other researchers to replicate and extend our work.

We present first results from a new agent-based model (ABM) of a sports-betting exchange (such as those operated by BetFair, BetDaq, and SMarkets, among other companies) in which each agent holds a dynamically varying opinion about some uncertain future event (such as which competitor will win a particular horse race) and in which all agents interact with the betting exchange to find counterparties holding an opposing view with whom they can then enter into a bet with. We extend methods from Opinion Dynamics (OD) research to give each agent an opinion at any particular time which is influenced partially by local interactions with other agents (as is common in the OD literature), partially by globally available information (as published to all by the betting exchange) and partially by the progressive reduction in uncertainty in the system (i.e., eventually all agents know which horse has won the race). Our work here is motivated by the prize-winning ICAART2021 paper of Lomas & Cliff, who integrated OD methods with ABMs of financial markets to explore issues in Narrative Economics, an approach recently proposed and popularised by Nobel Laureate Robert Shiller, but here we explore a significantly different type of market: a betting market (which has strong similarities to a financial market for tradable derivative contracts such as futures or options). The novel contributions of this paper are centred on the extension of OD methods to situations in which there is a mix of local and global influence, and in which uncertainty progressively reduces to zero. We present results from our initial proof-of-concept implementation. The Python source-code for our ABM is freely available on Github for other researchers to replicate and extend the work reported here.

In seeking to explain aspects of real-world economies that defy easy understanding when analysed via conventional means, Nobel laureate Robert Shiller has since 2017 introduced and developed the idea of Narrative Economics, where observable economic factors such as the dynamics of prices in asset markets are explained largely as a consequence of the narratives (i.e., the stories) heard, told, and believed by participants in those markets. Shiller argues that otherwise irrational and difficult-to-explain behaviors, such as investors participating in highly volatile cryptocurrency markets, are best explained and understood in narrative terms: people invest because they believe, because they have a heartfelt opinion, about the future prospects of the asset. In this paper we describe what is, to the best of our knowledge, the first ever agent-based modelling platform that allows for the study of issues in narrative economics. We have created this by integrating and synthesizing research in two previously separate fields: opinion dynamics (OD), and agent-based computational economics (ACE) in the form of minimally-intelligent trader-agents operating in accurately modelled financial markets. We show here for the first time how long-established models in OD and in ACE can be brought together to enable the experimental study of issues in narrative economics, and we present initial results from our system. The program-code for our simulation platform has been released as freely-available open-source software on GitHub, to enable other researchers to replicate and extend our work.

Financial markets populated by human traders often exhibit so-called "market impact", where the prices quoted by traders move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., "block") buy or sell order in the market: traders in the market know that a block buy order is likely to push the price up, and that a block sell order is likely to push the price down, and so they immediately adjust their quote-prices accordingly. In most major financial markets nowadays very many of the participants are ``robot traders", autonomous adaptive software agents, rather than humans. This paper addresses the question of how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. This is desirable because impact-sensitive trader-agents will get a better price for their transactions when block orders arrive, and because such traders can also be used for more accurate simulation models of real-world financial markets. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, called ISHV, which was the first such trader-agent to exhibit this market impact effect. ISHV does this via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). Our results demonstrate that the new imbalance-sensitive trader-agents introduced in this paper do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. We have made the source-code for our work reported here freely available on GitHub.

We analyse results from over 3.4million detailed market-trading simulation sessions which collectively confirm an unexpected result: in markets with dynamically varying supply and demand, the best-performing automated adaptive auction-market trading-agent currently known in the AI/Agents literature, i.e. Vytelingum’s Adaptive-Aggressive (AA) strategy, can be routinely out-performed by simpler trading strategies. AA is the most recent in a series of AI trading-agent strategies proposed by various researchers over the past twenty years: research papers contributing major steps in this evolution of strategies have been published at IJCAI, in the Artificial Intelligence journal, and at AAMAS. The innovative step taken here is to brute-force exhaustively evaluate AA in market environments that are in various ways more realistic, closer to real-world financial markets, than the simple constrained abstract experimental evaluations routinely used in the prior academic AI/Agents research literature. We conclude that AA can indeed appear dominant when tested only against other AI-based trading agents in the highly simplified market scenarios that have become the methodological norm in the trading-agents academic research literature, but much of that success seems to be because AA was designed with exactly those simplified experimental markets in mind. As soon as we put AA in scenarios closer to real-world markets, modify it to fit those markets accordingly, and exhaustively test it against simpler trading agents, AA’s dominance simply disappears.

We report on results from experiments where human traders interact with software-agent traders in a real-time asynchronous continuous double auction (CDA) experimental economics system. Our experiments are inspired by the seminal work reported by IBM at IJCAI 2001, where it was demonstrated that software-agent traders could consistently outperform human traders in real-time CDA markets. IBM tested two trading-agent strategies, ZIP and a modified version of GD, and in a subsequent paper they reported on a new strategy called GDX that was demonstrated to outperform GD and ZIP in agent-vs.-agent CDA competitions, on which basis it was claimed that GDX ``...may offer the best performance of any published CDA bidding strategy''. In this paper, we employ experiment methods similar to those pioneered by IBM to test the performance of Vytelingum's ``Adaptive Aggressive'' (AA) algorithmic traders. The results presented here confirm Vytelingum's claim that AA outperforms ZIP, GD, and GDX in agent-vs-agent experiments. We then present the first results from testing AA against human traders in human-vs.-agent CDA experiments, and demonstrate that AA's performance against human traders is superior to that of ZIP, GD, and GDX. We therefore claim that, on the basis of the available evidence, AA may offer the best performance of any published bidding strategy.

Modern large-scale date centres, such as those used for cloud computing service provision, are becoming ever-larger as the operators of those data centres seek to maximise the benefits from economies of scale. With these increases in size comes a growth in system complexity, which is usually problematic. There is an increased desire for automated "self-star" configuration, management, and failure-recovery of the data-centre infrastructure, but many traditional techniques scale much worse than linearly as the number of nodes to be managed increases. As the number of nodes in a median-sized data-centre looks set to increase by two or three orders of magnitude in coming decades, it seems reasonable to attempt to explore and understand the scaling properties of the data-centre middleware before such data-centres are constructed. We presented SPECI, a simulator that predicts aspects of large-scale data-centre middleware performance, concentrating on the influence of status changes such as policy updates or routine node failures. The initial version of SPECI was based on the assumption (taken from our industrial sponsor, a major data-centre provider) that within the data-centre there will be components that work together and need to know the status of other components via "subscriptions" to status-updates from those components. We used a first-approximation assumption that such subscriptions are distributed wholly at random across the data centre. In this present paper, we explore the effects of introducing more realistic constraints to the structure of the internal network of subscriptions. We contrast the original results from SPECI with new results from simulations exploring the effects of making the data-centre's subscription network have a regular lattice-like structure, and also semi-random network structures resulting from parameterised network generation functions that create "small-world" and "scale-free" networks. We show that for distributed middleware topologies, the structure and distribution of tasks carried out in the data centre can significantly influence the performance overhead imposed by the middleware.

The zero-intelligence plus (ZIP) adaptive automated trading algorithm has been demonstrated to outperform human traders in experimental studies of continuous double auction (CDA) markets populated by mixtures of human and ldquosoftware robotrdquo traders. Previous papers have shown that values of the eight parameters governing behavior of ZIP traders can be automatically optimized using a genetic algorithm (GA), and that markets populated by GA-optimized traders perform better than those populated by ZIP traders with manually set parameter values. This paper introduces a more sophisticated version of the ZIP algorithm, called ldquoZIP60,rdquo which requires the values of 60 parameters to be set correctly. ZIP60 is shown here to produce significantly better results in comparison to the original ZIP algorithm (called ldquoZIP8rdquo hereafter) when a GA is used to search the 60-dimensional parameter space. It is also demonstrated here that this works best when the GA itself has control over the dimensionality of the search-space, allowing evolution to guide the expansion of the search-space up from 8 parameters to 60 via intermediate steps. Principal component analysis of the best evolved ZIP60 parameter-sets establishes that no ZIP8 solutions are embedded in the 60-dimensional space. Moreover, some of the results and analysis presented here cast doubt on previously published ZIP8 results concerning the evolution of new ldquohybridrdquo auction mechanisms that appeared to be improvements on the CDA: it now seems likely that those results were actually consequences of the relative lack of sophistication in the original ZIP8 algorithm, because ldquohybridrdquo mechanisms occur much less frequently when ZIP60s are used.

The "ZIP" adaptive trading algorithm has been demonstrated to outperform human traders in experimental studies of continuous double auction (CDA) markets. The original ZIP algorithm requires the values of eight control parameters to be set correctly. A new extension of the ZIP algorithm, called ZIP60, requires the values of 60 parameters to be set correctly. ZIP60 is shown here to produce significantly better results than the original ZIP (called "ZIP8" hereafter). A genetic algorithm (GA) is used to search the 60-dimensional ZIP60 parameter space, and it finds parameter vectors that yield ZIP60 traders with mean scores significantly better than those of ZIP8s. This paper shows that this optimizing evolutionary search works best when the GA itself controls the dimensionality of the search-space, so that the search commences in an 8-d space and thereafter the dimensionality of the search-space is gradually increased by the GA until it is exploring a 60-d space. Furthermore, the results from ZIP60 cast some doubt on prior ZIP8 results concerning the evolution of new 'hybrid' auction mechanisms that appeared to be better than the CDA.

In a previous article, we introduced a number of visualization techniques that we had developed for monitoring the dynamics of artificial competitive coevolutionary systems. One of these techniques involves evaluating the performance of an individual from the current population in a series of trials against opponents from all previous generations, and visualizing the results as a 2D grid of shaded cells or pixels: qualitative patterns in the shading can indicate different classes of coevolutionary dynamics. As this technique involves pitting a current individual against ancestral opponents, we referred to the visualizations as CIAO plots. Since then, a number of other authors studying the dynamics of competitive coevolutionary systems have used CIAO plots or close derivatives to help illuminate the dynamics of their systems, and it has become something of a de facto standard visualization technique. In this very brief article we summarize the rationale for CIAO plots, explain the method of constructing a CIAO plot, and review important recent results that identify significant limitations of this technique.

No abstract available.

A previous paper by D. Cliff (see ibid., 2002) demonstrated that a genetic algorithm could be used to automatically discover new optimal auction mechanisms for automated electronic marketplaces populated by software-agent traders. Significantly, the new auction mechanisms are often unlike traditional mechanisms designed by humans for human traders; rather, they are peculiar hybrid mixtures of established styles of mechanism. This previous work used software agents running the ZIP trader algorithm (recently shown to outperform human traders). We provide the first demonstration that qualitatively similar results (i.e., non-standard hybrid mechanism designs being optimal) are also given when similar experiments are performed using a different trader algorithm, namely Gode & Sunder's (1993) ZI-C traders. Thus, the paper is the first to offer significant evidence that evolved hybrid auction mechanisms may be found that out-perform traditional market mechanisms for many styles of trader-agent.

A continuous space of auction mechanisms is explored via a genetic algorithm, with ZIP artificial trading agents ope rating in the evolved markets. The space of possible auction-types includes the Continuous Double Auction and also two purely one-sided mechanisms, yet hybrids of these auction types are regularly found to give the most desirable market dynamics.

An artificial life entertainment software product called Creatures was released in Europe in late 1996 and in the United States and Japan in mid-1997. When installed on a domestic computer (PC or Macintosh), each Creatures CD-ROM creates a virtual world in which autonomous software agents exist. The agents, known as "norn," interact with the human user, with each other, and with objects in their virtual world. Each norn coordinates perception and action via its own modular recurrent neural network. Each network has Hebbian learning, plus diffuse modulation of activity via a "hormonal" system that is part of that norns "biochemistry." Details of each norns neural network and biochemistry are genetically specified, and norns can breed via sexual reproduction. In the reproduction process, genetic material may be mutated and may also be subjected to "gene duplications" that enable potentially unlimited increases in complexity of the norns' design. Over 500,000 Creatures CD-ROMS have now been sold. As each installed copy of Creatures can support 5 to 10 simultaneously existing individual norns, it seems reasonable to estimate that there are up to 5 million norns existing in the "cyberspace" provided by the global Creatures user community. Continued growth of the global norn population, to figures measured in tens of millions, is quite likely. Although a commercial product, the Creatures digital ecosystem should be of interest to artificial life scientists. There are obvious parallels with Yaeger's PolyWorld and Ray's NetTierra systems. This article provides a detailed discussion of the links between the artificial life literature and the technology used in Creatures and includes anecdotal discussion of the "digital naturalism" witnessed on the many independent websites maintained by Creatures enthusiasts.

The continuous double-auction (CDA) is a powerful market mechanism, noted for its speed and efficiency, and is the mechanism underlying the organization of open-outcry trading pits at major international derivatives markets. Cliff and Bruten (1997) demonstrated that software trading agents need more than zero intelligence to give human-like price-equilibration behavior and presented results from experiments with simple adaptive trading agents in CDA markets. These agents give very good performance on standard measures of trading activity such as allocative efficiency, Smith's (1962) /spl alpha/ measure, and profit dispersion, but only when parameters governing the adaptation mechanism are set to appropriate values. Determining good or optimal combinations of parameters by hand is possible, but can be labor-intensive. The paper presents the first results from using a genetic algorithm to optimize key parameters governing adaptation in the trading agents. It is shown that a simple genetic algorithm, in combination with an appropriate evaluation function, can rapidly deliver good parameter settings from random initial-value conditions.