Capturing Social Embeddedness: a constructivist approach--论文代写范文精选

这篇essay代写范文旨在确定现象可能有用的指示，以及代理社会嵌入的程度。为了达到这一目标，塑造这个模型的方法是采用将想法从建构主义创新，提供了一个简短的概述。下面的essay代写范文进行详述。

Abstract 
A constructivist approach is applied to characterising social embeddedness and to the design of a simulation of social agents which displays the social embedding of agents. Social embeddedness is defined as the extent to which modelling the behaviour of an agent requires the inclusion of the society of agents as a whole. Possible effects of social embedding and ways to check for it are discussed briefly. A model of co-developing agents is exhibited, which is an extension of Brian Arthur ’s ‘El Farol Bar’ model, but extended to include learning based upon a GP algorithm and the introduction of communication. Some indicators of social embedding are analysed and some possible causes of social embedding are discussed. 
Keywords: simulation, embedding, agents, social, constructivism, co-evolution

Introduction 
In the last decade there has been a lot of attention paid to the way the physical situation of a robot affects its behaviour* . This paper focuses on the analogous importance of the social situation to an agent. It aims to identify phenomena that may be usefully taken to indicate the extent to which agents are socially embedded. In particular, it aims to do this for an artificial simulation involving co-evolving agents. In order to do this a modelling approach is adopted which takes ideas from the several varieties of constructivism. The first section presents a brief overview of constructivism and its relevance to simulations of social agents. Then there is a section discussing the idea and possible effects of social embeddedness. A model illustrating differing degrees of social embeddedness is then exhibited. Both some general results and a couple of more detailed case studies are then presented. The paper ends with a short discussion of the possible causes of social embeddedness.

Constructivism and AI 
Constructivism, broadly conceived, is the thesis that knowledge can not be a passive reflection of reality, but is more of an active construction by an agent. Although this view has its roots in the ideas of Kant, the term was first coined by Piaget [27] to denote the process whereby an individual constructs its view of the world. Extrapolating from this is Ernst von Glasersfeld’s ‘radical constructivism’ [19] which approaches epistemology from the starting point that the only knowledge we can ever have is so constructed. In cybernetics it was used by Heinz Von Foerster [17], who pointed out that an organism can not distinguish between perceptions of the external world and internally generated signals (e.g. hallucinations) on a priori grounds, but retains those constructs that help maintain the coherence of the organism over time (since those that do not will have a tendency to be selected out) .

Constructivism has been taken up by some researchers in artificial intelligence and artificial life (e.g. [9, 28, 29]) as an approach to building and exploring artificially intelligent agents from the bottom up. Here, instead of specifying an architecture in detail from a priori considerations, the mechanisms and cognition of agents are developed using self-organisational and evolutionary mechanisms as far as possible. For this approach to be viable the agents must be closely situated in its target environment, since is it the serendipidous exploitation of features of its environment and the strong practical interaction during development which makes it effective (this distinguishes it from a lot of work in ‘Artificial Life’). This is in contrast to what might be called an ‘engineering approach’ to artificial agents, where the agents are designed and set-up first and then let loose to interact with other such agents in order to achieve a specified goal. Constructivism in AI can be seen as an extension of the work of Rodney Brooks [5], but instead of the development of the organism happening through a design and test cycle done by human designers, the development is achieved via self-organisational and evolutionary processes acting on an agent situated in its environment. 

This paper is constructivist in three different ways. Firstly, the approach to characterising social embeddedness is through properties of our constructs of the systems we are investigating. Secondly, the exhibited model is built in a constructivist AI style, in that: the content and development of an agent’s cognition is specified as loosely as possible, where constructs are grounded in their effect upon the agent in conjunction with other agent’s actions; and also that the meaning of the agent’s communication is unspecified, so the effect of such communication is grounded in its use in practice and its development in the language-games that the agents appear to play. Lastly, constructivism is posited as a sensible explanation of the observed behaviour of the agents in the model described and hence, by analogy, as a possible explanatory tool for other social situations.

Characterising Social Embeddedness 
In attempting to elucidate the concept of ‘social embeddedness’, one faces the problem of where to base one’s discussion. In sociology it is almost an assumption that the relevant agents are ultimately embedded in their society – phenomena are described at the social level and their impact on individual behaviour is sometimes considered. This is epitomised by Durkheim, in that he claims that some social phenomena should be considered entirely separately from individual phenomena [10]. Cognitive science has the opposite perspective – the individual’s behaviour and processes are primitive and the social phenomena may emerge as a result of such individuals interacting. This split is now mirrored in the world of computational agents.’

In traditional AI it is the individual agent’s mental processes and behaviour that are modelled and this has been extended to considerations of the outcomes when such autonomous agents interact. In Artificial Life and computational organisational theory the system as a whole is the focal point and the parts representing the agents tend to be relatively simple. I wish to step back from disputes as to the extent to which people (or agents) are socially embedded to one of the appropriateness of different types of models of agents. From this view-point, I want to say that an agent is socially embedded in a collection of other agents to the extent that it is more appropriate to model that agent as part of the total system of agents and their interactions as opposed to modelling it as a single agent that is interacting with an essentially unitary environment. Thus I have characterised social embeddedness as aconstruct which depends on ones modelling goals, since these will affect the criteria for the appropriateness of models. It contrasts modelling agent interaction from an internal perspective (the thought processes, beliefs etc.) with modelling from external vantage (messages, actions, structures etc.). This is illustrated below in figure 1.

This is not an extreme ‘relativist’ position since, if one agrees the modelling framework and criteria for model selection, the social embedding of agents within a collection of agents can be unambiguously assessed. Notice that criteria for model acceptability can include many things other than just its predictive accuracy, for example: complexity [12]. It is the inevitability of these other concerns which forces us to relativise this approach as one concerning the appropriateness of our constructs (along with the different modelling goals and frameworks). For example, a computer may be able to find obscure and meaningless models which (for computational purposes) separates out the behaviour of a single agent from its society (using something like genetic programming), which are totally inaccessible to a human intelligence.

At first sight this seems a strange way to proceed; why not define social embeddedness as a property of the system, so that the appropriate modelling choices fall out as a natural result? The constructivist approach to characterising social embedding, outlined above, results from my modelling goals. I am using artificial agents to model real social agents (humans, animals, organisations etc.), and so it is not enough that the outcomes of the model are verified and the structure validated (as in [25]) because I also wish to characterise the emergent process in a meaningful way – for it is these processes that are of primary interest. This contrasts with the ‘engineering approach’ where the goal is different – there one is more interested in ensuring certain specified outcomes using inter-acting agents. 

When observing or modelling social interaction this meaning is grounded in the modelling language, modelling goals and criteria for model acceptability (this is especially so for artificial societies). The validation and verification of models can not be dispensed with, since they allow one to decide which are the candidate models, but most of the meaning comes from the modelling framework. The complexity of social phenomena (including, as we shall see in artificial societies) forces a ‘pragmatic holism’ upon us – that is, regardless of whether one is anin principle holist or an in principle reductionist, in practice we don’t have the choice [11]. In simpler physical situations it may be possible to usefully attribute phenomena to an external reality but in social modelling we have to make too many choices in order to make progress. The proof of this particular pudding will ultimately be in the eating; whether this approach helps us obtain useful models of social agents or not.（essay代写)

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