La socialización bilingüe en contextos de migración

Conventional competitive equilibrium model of economics (Arrow and Debreu 1954) view financial markets as competitive systems and assert that competitions of market players will eventually result in equilibriums, e.g., supply and consume, assets pricing or allocation of resources etc. One of the strong assumptions that sustain the standard competitive paradigm of economics is the assumption of the existence of perfect information in markets. Under this assumption, markets are fully informationally efficient. Information is disseminated efficiently and perfectly throughout the

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economy. All participants of the market have the same information, i.e., “I know what you know and you know what I know”. The efficient market hypothesis, see section 3.1, is one such market model based on perfect information and says that stock prices convey all the relevant information from the informed to the uninformed, i.e., stock prices reflect a stock’s intrinsic value, hence, any speculation on stock prices is pointless. Rational expectations, see section 3.5, is another example of such perfect information paradigm that models market players with perfect rationality.

The spirit of the standard competitive paradigm centres on the word “equilibrium”.

However, many phenomena we observed from real world markets objects to this

“equilibrium” mechanism. For example, the persistent and large-scale unemployment in labour markets or the excessive volatility in assets prices from financial markets and property markets. The competitive paradigm of economics cannot explain such phenomena where markets failed to achieve the desired equilibrium. Information economics (Stiglitz 2003) attempts to explain these market failures by questioning the assumption of the existence of perfect information in competitive paradigms. Early work in information economics mainly dealt with how markets overcame problems of information asymmetries (Akerlof 1970; Spence 1974; Rothschild and Stiglitz 1976).

Later work was more focused on how actors in markets create such information problems. Grossman and Stiglitz (1976, 1980) criticises the efficient market hypothesis by showing that, when information is costly to collect, stock prices necessarily aggregate information imperfectly. The author argued that under perfect competition, stock prices reflect all of the information as stated in the efficient market

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hypothesis. But if the market is perfectly efficient, no one will have any incentive to gather any information. Investors can get all the information by just looking at prices.

If no one gathers information, however, then stock prices do not have any information to reflect. This paradox lays the basis for the argument that imperfect information in markets is likely to be the rule rather than the exception. Therefore, markets are neither perfectly efficient nor completely inefficient. Stock prices are essentially the aggregators and disseminators of information. The authors concluded that the costliness of information eventually results in the impairment of informational efficiency in the market and asymmetrically informed stock traders. This implies better informed investors will have opportunities to take advantage of inferior investors; hence, speculation is not necessarily meaningless when information is costly and the speculative behaviour of traders causes volatility in stock prices. The study of information economics also extends into different fields in economy, for example, corporate governance under an imperfect information paradigm (Shleifer and Vishny 1989) or welfare economics in imperfect economies (Arrow et al. 2003).

Arnott et al. (2003) describes information economics as a change from the perfect information paradigm in competitive models to an imperfect information paradigm for economics. It points out that many features of real-world transactions can only be understood in an imperfect information framework. The imperfect information paradigm provides an alternative way that helps people understand economic phenomena much better. Under the same principle, when we struggle with the adaptation problem of the conventional research paradigms in artificial intelligence,

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where artificial intelligence is regarded as a complete perfect end product, we cannot help thinking if artificial intelligence research needs a new perspective: an imperfect perspective? The answer is yes. If economics for an imperfect world has been proved important in solving economic problems, artificial intelligence for an imperfect world is also demanded in order to understand intelligence better. Minsky (1986) described his well-known society of minds theory as following:

“…What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle. Our species has evolved many effective although imperfect methods, and each of us individually develops more on our own. Eventually, very few of our actions and decisions come to depend on any single mechanism. Instead, they emerge from conflicts and negotiations among societies of processes that constantly challenge one another…”

Minsky’s description on intelligence reveals intelligence as an emergent property in social environments, rather than a single isolated entity. We cannot talk about the development of intelligence without considering the incomplete environment that the intelligence resides in. If the world we are living in is imperfect, how can we expect AI to be a perfect end product, which is unable to cope with an imperfect and constantly changing environment? A change from the conventional perfect paradigm to an imperfect paradigm is needed in artificial intelligence research, which leads to our proposal of imperfect evolutionary systems. Minsky’s description also points out that the development of intelligence is not only processes of individual learning but

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also from interactions among societies of processes that constantly challenge each other, i.e. social learning. We believe the integrated individual learning and social learning architecture examined in this chapter provides an effective mechanism for implementing imperfect evolutionary systems.

3.7 Summary

Three major conclusions from this chapter are:

1. Due to its high complexity and heterogeneity, the stock market serves as an excellent test bed for modelling evolutionary artificial lives.

2. Changing from the conventional perfect information paradigm to an imperfect information paradigm points out an alternative way for artificial intelligence research.

3. Integrated individual and social learning paradigm provides an effective mechanism for developing imperfect evolutionary systems.

In this chapter, we also looked at various investment strategies related to the stock market including the buy and hold strategy and various fundamental and technical trading strategies. We discussed the applications of evolutionary computation techniques in terms of financial engineering. We described various techniques in modelling stock markets as evolutionary adaptive systems including learning classifier systems, genetic programming and artificial neural networks. Although most researchers who used learning classifier systems or genetic programming for ASMs argued that these two techniques are more easily interpreted and hence human

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readable compared with artificial neural networks, there are still issues that need careful consideration regarding LCS and GP. In learning classifier systems, the rules of classifiers need to be hand-coded by system developers. For example, in Schulenberg and Ross (2001), their artificial trader (type 1) used a rule with one condition as: , which says check if the current price is higher than the 30-day moving average multiplied by 1.025. Here, people could ask why was a value of 1.025 chosen as the coefficient and not 1.035? The design of rules in classifier systems is entirely dependent on the developers’ and other experts’ knowledge on a particular problem.

* 30

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Regarding genetic programming, the exponential increase in the size of genetic decision trees is a major technical issue in GP applications (see section 2.7.4). Chen (2001) discusses most of the major technical issues in applying GP in agent-based computational economics with regards to the selection of the function set and the terminal set, semantic restriction, genetic operators and architecture, and argued that unless the above issues are well addressed, genetic programming is not well grounded in consideration of human behaviour, and it would be premature to claim that GP has modelled a population of agents learning over time. In the following chapters, for the implementations of imperfect evolutionary systems, we choose artificial neural networks for the modelling of artificial agents for several reasons: First, artificial neural networks most closely emulate the human brain; Secondly, artificial neural networks do not require human expertise in solving a problem (see Blondie24 in

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section 2.6); Third, artificial neural networks have the capability to model highly non-linear functions and noise tolerant for forecasting problems (see section 3.4.2).

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Chapter 4

Imperfect Evolutionary Systems

This chapter gives a formal description of our imperfect evolutionary systems. We use two examples, i.e. stock market and game playing, to illustrate our definition of imperfect evolutionary systems. An integrated individual learning and social learning paradigm is described as one of the possible mechanisms for developing an imperfect evolutionary system.