In this chapter, we have given an overview of the state of the art in research regarding convention emergence, and identified a number of limitations that impede a full quantitative understanding of convention emergence in large de- centralised populations of individuals. In response, we have defined a conceptual framework for describing open MAS with conventions, and illustrated how ex- isting convention emergence formulations can be easily expressed in it to enable comparison. We propose a new definition of conventions that allows for the co- existence of multiple conventions and facilitates analysis of conventions before they are traditionally accepted as being established. Our proposed set of met- rics for describing convention quality, adherence and stability aims to support analysis of the middle to latter stages of the life cycle of conventions. Our future work will aim to analyse typical models of convention emergence within our con- ceptual framework. It is our expectation that such analysis will yield detailed insight into the nature of conventions, allowing us to design novel mechanisms for (i) determining which conventions are desirable, (ii) identifying those we
wish to destabilise, (iii) supporting the emergence of desirable conventions, and (iv) determining a configuration of coordinated and co-existing conventions in populations where we may not be able to or may not wish to establish a single convention.
The conceptual framework presented in this chapter is a first step, in which we re-orient the traditional agent-based perspective regarding conventions to
more closely fit the view of regularities as suggested by Lewis (1969), in which
an agent has a significant probability of repeatedly choosing a given action. As such, we consider a wider variety of behaviour as conventional, and can use our metrics of quality, adherence and stability to determine how desirable each convention is. Our metrics also provide natural ways to quantify the conventions at which it might be useful to target supporting mechanisms, and it is our aim
to evaluate this in future research. We envisage useful applications for our
framework in a wide variety of domains, including social media and marketing, mechanisms for protecting conventions from external invasion, and mechanisms for destabilising undesirable conventions. In subsequent chapters in this thesis, we adopt the syntax of our formalism for describing the agent interaction models that we use. Appendix B also provides examples of how the formalism can describe common models of convention emergence.
There have been very few attempts to provide a unifying framework for con- vention emergence in open MAS. The most applicable proposal, that of Walker and Wooldridge (1995), oriented its formalism with respect to runs of a system, implying a well-defined start and end point for a system. This significantly re- duces its applicability to open MAS. Walker and Wooldridge also defined very few metrics for quantifying properties of conventions, and focused on conven- tion convergence and the number of strategy changes that agents make. These metrics assume that a single convention is the ideal or attainable goal, which may not be the case. The formalism described in this chapter does not rely on a notion of runs, does not assume a single convention is ideal or attainable, and defines many more metrics to quantify a wide variety of convention properties.
To our knowledge, there are no other convention frameworks that address these issues and are therefore suited to convention emergence in open MAS.
The work in this chapter suggests a wide variety of directions for future re- search, including evaluating targeting mechanisms for convention emergence at a specific group (i.e. users, non-users, and so on), extending the work on link- ing topological structure with convention behaviour, and examining the set of metrics described here in established models of convention emergence to deter- mine whether or not they provide any insight into the behaviour of conventions. These extensions are outside the scope of this thesis, and in subsequent chap-
ters we focus on investigating the manipulation of conventions. Specifically,
Chapter 5 focuses on whether conventions can be manipulated in open MAS at all, Chapter 6 determines the extent to which knowledge of the underlying network structure can be exploited, and Chapter 7 determines the extent to which conventions can be manipulated in the middle and latter stages of the life cycle.
Manipulating conventions using Influencer Agents
In the previous chapter, we discussed a number of limitations with the current theory of conventions and identified directions for future research. One area for investigation which has seen limited attention is the manipulation of conven- tions. In this chapter, we investigate how conventions might be manipulated in open MAS. We propose the Influencer Agent (IA) mechanism, in which a small proportion of agents with specific goals and strategies are inserted into the pop- ulation in order to manipulate which convention the society adopts. We show that small proportions can be highly effective, and demonstrate that exploiting topological features can improve efficacy. IAs are a fundamental mechanism in the remaining chapters of this thesis: in Chapter 6, we investigate how exploit- ing knowledge of the underlying network structure can improve IA efficacy, and in Chapter 7 we show how IAs can be used to manipulate conventions in the middle and latter stages of the convention lifecycle, and empirically analyse the efficacy of equipping IAs with sanctions and incentives.
5.1
Introduction
Conventions are known to encourage high levels of coordination, but efficiently
manipulating which convention emerges remains an open research problem.
Considerations of limited knowledge of society characteristics, time variance, and computational difficulty often preclude the ability to generate and impose
high quality conventionsa priori. Mechanisms that encourage online generation
and adoption of appropriate conventions often assume the ability to universally incorporate additional structures into agent or society architecture. In this the- sis, we assume heterogeneous ownership of agents (Chapter 1) and that agents are able to join and leave freely at run-time. Subsequently, we cannot rely on adding additional structures into agent architectures, or make any guarantees that the proportion of agents adopting a particular mechanism will be sufficient to ensure feasibility. Similarly, we cannot assume that we can impose society- level structures on the system. As such, we require a model of how purely rational agents might be manipulated into adopting high quality conventions and otherwise aided in increasing levels of coordination within the system.
In this chapter, we propose inserting a small number of agents, with specific conventions and strategies, such that the population as a whole, through their normal rational selection of actions, is guided towards the adoption of high
quality conventions. We call these inserted agentsInfluencer Agents (IAs), and
show that a small proportion of IAs in an artificial society can efficiently aid the generation and propagation of high quality conventions. This mechanism of manipulating convention emergence does not require any assumptions of agent behaviour beyond rationality, and to our knowledge does not currently exist in this form in the literature.
To demonstrate the IA mechanism, we adopt a model of convention emer-
gence in the language coordination domain, primarily defined by Salazar et
al. (2010b). Agents are associated with individual lexicons, mappings from
communicating and sharing partial lexicons. The emergence of a shared lexicon constitutes the emergence of a convention. For a full description of the model,
see Section 5.4. We rely extensively on Salazaret al.’s work to demonstrate our
IA mechanism. The model that they present incorporates realistic assumptions, such as complex connecting topologies and very large convention spaces. Very few models of convention emergence use such large convention spaces, and by using this property the demonstration of IA efficacy is less likely to be due to
chance. Furthermore, Salazar et al. have presented extensive results showing
that simple agent strategies can lead to high-quality convention emergence, and the quasi-continuous nature of the convention space (as opposed to the discrete
convention spaces of the majority of models) allows us to measure the extent
of an IA’s efficacy, as opposed to a binary observation of whether they were successful or not.
We discuss the IA concept, the research that inspired it, and possible strate- gies with which IAs can be equipped in Sections 5.2 and 5.3. Section 5.4 details our experimental setup, and Section 5.5 establishes baseline model behaviour and presents results demonstrating the efficacy of the IA concept. Finally, Sec- tion 5.6 discusses conclusions and directions for future work.