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Rosaria Conte and Frank Dignum (2001)

From Social Monitoring to Normative Influence

Journal of Artificial Societies and Social Simulation vol. 4, no. 2,

To cite articles published in the Journal of Artificial Societies and Social Simulation, please reference the above information and include paragraph numbers if necessary

Received: 01-Nov-00      Accepted: 01-Feb-01      Published: 31-Mar-01

* Abstract

This paper is intended to analyse the concepts involved in the phenomena of social monitoring and norm-based social influence for systems of normative agents. These are here defined as deliberative agents, representing norms and deciding upon them. Normative agents can use the norms to evaluate others' behaviours and, possibly, convince them to comply with norms. Normative agents contribute to the social dynamics of norms, and more specifically, of norm-based social control and influence. In fact, normative intelligence allows agents to
  1. Check the efficacy of the norms (the extent to which a norm is applied in the system in which it is in force), and possibly
  2. Urge their fellows to obey the norms.

The following issues are addressed:

norms, multi agent systems, imitation, social control, social cognition

* Introduction

As is illustrated by some authors (Tambe 1997; Kaminka & Tambe, 1998), coordination between agents[1] in a multi-agent system is far from trivial. Although it is possible to have agents work on a collective plan or cooperate (and even compete) according to some established protocol, strange things happen when one of the agents does not fulfil her part of the bargain (maybe due to some external circumstances). For example, a helicopter disrupts the complete plan by flying into enemy territory by accident, because it misses a delimitation point. The other helicopters do not notice this because their part of the plan is not (directly) disrupted and they are not monitoring (controlling or checking) the behaviour of the other helicopters. This shows that a cooperative plan might benefit greatly from the agents' capacity to check one another, thereby preventing misdoings, which might compromise the collective result even without impairing the others' activity.

It seems that an agent that is part of a multi-agent system should also be aware of the other agents in the system and of the relations that exist between the agents and the rules that govern these relations. These rules and relations determine the balance between the autonomy of the agent and the inter-dependencies between the agents. In human societies, these rules are determined in large part through (formal or informal) social norms. They form a very subtle mechanism according to which humans can be members of social groups, but at the same time autonomous. They can compete with other humans (as in commerce) while at the same time obeying some social rules (like not committing fraud). The main thesis of this paper is that the normative social system that exists in human society should function as a starting point for constructing social rules in multi-agent systems. Only when we have some understanding about the human social system can we determine which parts are relevant for multi-agent systems and how they should be implemented.

This paper is intended to give a first analysis of the concepts involved in the phenomena of social monitoring and norm-based social influence for multi-agent systems with normative agents. Normative agents are here defined as Belief/Desire/Intention agents (that is, agents endowed with a set of mental properties necessary for deliberating) deciding upon norms. Throughout this paper, these properties will be referred to as normative intelligence. Although the goal is to use the concepts found in multi-agent systems, the analysis is mainly based on human societies and studies in the social sciences.

This work is part of a research project (cf. Conte et al., 1998) on the study of the emergence and spread of norms in intelligent agent societies, that aims to implement norms in BDI agents and run simulation experiments to check the global social advantages of normative intelligence. Such advantages may include an intelligent application of norms, the solution of conflicts between norms, the execution of contrary-to-duty obligations. A special effect of normative intelligence at the global level is norm-based social control. Normative intelligence enables agents to monitor one another's behaviours against existing norms. Agents endowed with normative intelligence (from now on, normative agents[2]) can use the norms they have accepted to evaluate others' behaviours and, possibly, convince their fellows to comply with norms. More generally, we have argued (Conte et al., 1998) that a theory of normative intelligence may contribute in two important ways to a scientific study of norms.

First, it may bridge the gap between legal and social norms. In AI, there are two main fields where normative concepts and phenomena abound:

A wide gap exists between these fields. They differ in many ways[3]. One important difference lies in their respective concept of a norm, which is mainly interpreted as a legal norm in the former field, and as a social convention in the latter field. In our view, such a gap may be filled thanks to a general theory of norms, whether legal or social, as prescriptions impinging on agents' minds before and in order to regulate their actions. Whether a norm is institutionally issued or emerges from interactional practice, it will actually rule the action of intelligent deliberative agents if they are enabled to recognise it and decide to obey or reject it. Norm-based decision-making is essentially common to both legal and social norms, while of course they differ in their origins and dynamics. A fundamental step common to both types of decision-making is that of norm-recognition, which was regarded by some philosophers of law (Hart 1961) as crucial for legal norms. Agents that recognise norms tend to enforce and propagate them in their social environment. More or less deliberately, they act as norm issuers. Both legal and social norms profit from this non-institutional propagation. Thanks to normative intelligence, the difference between the social dynamics of legal and social norms is fairly attenuated. Another important aspect of the relation between these two sub-sets of norms, legal and social, is to what extent they may contribute to each other's development or evolution. Can social norms lead to the formation of legal norms, and can legal norms lead to social norms being established, and if so, over what time-scale? This paper is not the forum for addressing such a fundamental issue (but see again, Conte & Castelfranchi 1999, for a conceptual discussion of norms intended to bridge the gap between legal and social norms). However, it should not be forgotten that some crucial meta-norms (like the norm of reciprocity) are plausibly at the origin of most social and legal norms (see Ostrom 1998). This is not to say that legal and social norms necessarily share the same roots, since many legal norms may be issued for rather arbitrary and technical reasons. However, some normative principles, like reciprocity, seem to operate in both domains. Furthermore, legal norms may, and often do, generate new social values. The new legislative regulation of nicotine and smoking behaviour introduced in western countries has undoubtedly not only favoured a more responsible behaviour on the part of smokers with regard to their social surroundings, but is also associated with a different attitude with regard to people's own health and individual well being, which might be perceived as a new socially shared value evolving. Whether this is at the origin of the new legislation or a consequence of it is hard to say, although a co-evolutionary and interactive process seems more plausible.

Secondly, a theory of normative intelligence may help to understand several aspects of the social dynamics of norms, and more specifically:

In this paper, we will focus on normative control and influence, while the contribution of normative intelligence to norm formation and evolution will be discussed in future work. More specifically, we will address the following issues:

This paper is only a first step to work out a complete theory, and is mainly aimed at making explicit hypotheses for further computational and simulation work. Results will consist of possible contributions to:

In the next section, norm-based control will be investigated as a multi-level, evolutionary notion. It will be shown to be rooted in social monitoring and possibly lead to normative influence. In the following section, the concept of social monitoring will be examined in its main components: motivations, mental ingredients and effects. In section 4, social control will be defined as a step in the process of social influence, and its motivations, requirements and effects analysed. Interestingly, social control may not lead to the exercise of any influence, but to reconsidering one's own norms. The interesting question about when social control leads to social influence will be raised in section 5, where some answers will be discussed, and the mental ingredients and effects of normative influence will be analysed. In the final section, directions for future works will be pointed out.

* From Social Monitoring to Social Influence

One of the most influential views derived from social psychology states that social phenomena propagate thanks to imitation of others' behaviours (Bandura 1977). This view has had a vast influence in several social scientific fields, especially in evolutionary game theory (Weibull 1996) and in social dilemmas theory (Liebrand and Messick 1996). Conventions, norms, regularities, social structures and patterns of any sort (segregation, discrimination, opinion formation) are said to emerge from agents' imitation of either the most successful, or the most frequent, or simply the closest behaviours.

In the multi-agent systems field, several attempts have been made to check the distribution of conventions and social laws thanks to one or other of the above imitative strategies (Shoham and Tennenholtz 1995; 1997; Walker and Wooldridge 1995, etc.).

But imitation is not a sufficient explanation of social dynamics. Agents not only imitate passive models, whose influence is non-deliberate and even inadvertent, but also respond to active influence, deliberately exercised. Why do agents influence one another? So far, the main answer to this question (cf. Homans 1951; 1974) is in terms of a behavioural law of reinforcement: agents provide social approval or disapproval because they want to obtain approval in return or avoid disapproval. However, the reinforcement law cannot do justice to the complex mental processes that lead agents to influence one another. To see this, suffice it to consider that agents are able to discriminate between a normative and a non-normative influence, even if yielding to both will gain them some sort of approval, or any other type of reinforcement. Where does such a discerning capacity come from? Looking at the same phenomenon from the other side of the coin, agents are able to urge others to either conform to given behaviours or simply obey the norms, whatever they are. How could they assume such a discerning capacity in others, if they had none?

In the following, the concept of imitation is replaced by the concept of social monitoring, defined in its weakest and most general form as the agents' attitude to observe and use others' behaviours as a criterion for their own behaviours. Within this general process, we will pick one specific form of social monitoring, aimed at detecting existing norms.

Norm-based social control will be analysed as a subsequent step (Figure 1). Here, others' behaviours are monitored in order to check whether norms are effectively executed. This may be due to several reasons, only some of which, under given conditions, will lead to the further, conclusive step, namely norm-based influence. The reasons and ingredients of this process will be analysed in some detail at each of the steps of the process thus described.

Figure 1
Figure 1. From monitoring to influence

The general idea underlying our approach is that either agents use others' behaviour as a criterion, or they use norms as a criterion against which to evaluate others' behaviours, and in this case, they may try to modify it (norm-based influence) or not. In this paper, we will not go into the issue of norm formation. However, we would like to mention that the monitoring of other agents' behaviour against some average or standard behaviour might lead to the actual formation of a social norm that "abstracts" the average (or accepted) behaviour into a separate concept. Social control may lead one agent either to influence others to observe a norm or to its rejection. The question is when does norm-based social control lead to normative influence.

* Social Monitoring: A Multi-faceted and Scalable Notion

We will speak of social monitoring when one single agent observes and registers the behaviours of other agents belonging to a given group in order to adjust its own mental attitudes with regard to the norms or values or other standards in force among those agents. More specifically, an agent x "monitors" a set of agents Y (whether x is a member of Y or not), when it compares its behaviour, beliefs, goals, values, to those most frequently shown by the members of Y in order to find out similarities or discrepancies. In this sense, x is using Y's behaviours as a criterion for evaluating its own. The issue of social comparison is well-known to social psychologists. According to Festinger (1954), (social) agents have a tendency to compare themselves with their social environment in order to check possible discrepancies which might produce also discrepancies among their own cognitions (for example, her own values and the values of significant others). This view of social comparison is essentially belief-driven, and is based upon the intuition that agents need internal consistency.

The view of social monitoring proposed in this paper is rather action-, or goal-driven, and is based on the pragmatic assumption that agents turn to others for inputs under condition of imperfect or uncertain knowledge or instructions about how to behave in given conditions. In this conception, there are two possible goals of social monitoring.

In the following sections we will analyse the following aspects of social monitoring:


What are the agent's motivations to monitor its social surroundings? One immediate answer is suggested by the social learning literature (Bandura 1977): agents want to imitate others, or at least conform to those that they consider as valuable social models.

The follow-up question then is why do agents want to imitate others, and conform to their behaviours? Again, the classical social-psychological answer (Homans 1951; 1974) says that agents imitate others to obtain approval from them (see Figure 2).

Figure 2
Figure 2. Social learning

However, we believe that this answer accounts only part of the phenomenon. In other words, we think that the picture should include at least another path (see Figure 3).

Figure 3
Figure 3. Norm-oriented social monitoring

For example, agents might learn from one another what the consequences of norm violation are, that is punishment. In such a case, social surroundings do not motivate the individual agent to comply with the norm. Rather, they furnish the agent with a sort of experimental " testbed "[4] (see Conte & Paolucci 2001). Examples of this sort abound in social life: agents not only observe and learn given behaviours from one another, but also avoid the costs of a direct experiment, and learn the positive or negative (side-)effects of current plans/procedures. Agents find positive or negative models that allow them to instantiate their own standards, or they may infer standards and either accept or reject them. Obviously, to look for social approval can be considered as a precursor of the will to conform, and imitation as a forerunner of conformity. However, once norms and institutions have emerged, they allow for other cognitive objects to emerge, for example the expectation that norms exist and must be fulfilled. The fundamental difference between the models in Figures 2 and 3 is that norms in the first model are defined as the standard behaviour of a group. They have no existence of themselves. Therefore the only way for an individual to adjust to group behaviour is to adjust to the standard group behaviour. But what about all other norms, which may not coincide with the group standard behaviours? People know that safety belts should be fastened while driving even if such a norm is actually violated by a good proportion of the population if not the majority. The efficiency of norms would be greatly reduced if norms could be only deduced from standard behaviours.

Through social monitoring, agents find a fundamental source of information about which specific norms are impinging on a given set of agents. They will observe others to find out which are the norms in force in that particular social context. In case discrepancies between themselves and others are found, they may decide that others apply a set of norms that they were not aware of. Consider the case of a newcomer entering a social setting. How will she know what rules should she follow? One fundamental way to get this information without being perceived as an outsider is to observe others.

To sum up, we will speak about x social monitoring a given set of agents Y, when either

We believe that only this second case gives rise to x' s decision to reduce the discrepancy by inducing members of the group Y to change their behaviour. Only when x has an independent norm to which she believes the members of Y ought to comply, does she have the motivation to persuade them to observe that norm.

Mental requirements

The difference between the two possibilities mentioned above entails a substantive difference in terms of the mental requirements each involves. In the former case, x has no idea of abstract standards and accepts others' behaviours as concrete standards for its own behaviour. In the latter, x knows that a standard (e.g., a norm) exists and may operate on Y. Here, we will focus on x' s search for the norms in force in the reference group.


Mental consequences of social monitoring might include an update of the agent's values associated with given beliefs: for example, the agent may update the value of some beliefs concerning the frequency of given behaviours, the certainty of the related beliefs, and the diffusion or influence of given intentions and values[5].
At group level

If distributed social monitoring leads to a reduction of the perceived discrepancies, it is most likely to serve as a means to: All these features of a social group may prove advantageous for the members of the group in the long run, i.e. they may not profit from it directly, but may profit from the group in some future circumstances.
At the individual level

An important set of consequences concerns how x will use such norms once she has found them out. For example, x may apply them with no further need to monitor her social surroundings. Besides, x may generalise them to other contexts. More significantly, x may infer the rights and privileges that may be derived from them. Finally, x may go back to Y and evaluate its members with regard to those standards. The latter consequence leads us to what we will call norm-based social control.

* Norm-based Social Control

We will speak here of norm-based social control as a type of social monitoring (see Figure 4). Agents exercise social control when they monitor one another with regard to a (set of) norm(s) that they have acknowledged as such, and with regard to which they want to reduce existing behavioural discrepancies. More precisely, x exercises social control:

Figure 4
Figure 4. Social control as norm-based monitoring

Note that norms may be formed through social monitoring, but also arise from other inputs (see Figure 4).

Again, some of the questions raised with regard to social monitoring apply to the case of norm-based social control:

The likelihood of social monitoring leading to norm-compliance is a function of different quantitative dimensions, which include the degree of efficacy of the norms observed.

Norms under Social Control

Here, we will restrict the application of social control to:

This points to our analysis of norm acceptance (see Conte et al. 1998), where we modelled this process as consisting of a number of tests. One such test may give as an output that a norm is acknowledged as such (as issued by a legitimate authority, falling into the domain of competence of that authority). A subsequent test may give as an output that the acknowledged norm concerns x. In such a case x accepts n as a norm which concerns it.

In the case of social control, we hypothesise that agents are entitled and in fact do check one another with regard to acknowledged norms. Moreover, we hypothesise that this is the case even when agents are not directly concerned with the norm. So, a pedestrian will check the car drivers' compliance with the norms regulating the circulation of cars. In this case, driver and pedestrian have complementary roles: x is a beneficiary of the norms, and probably shares them, considering them as a valuable means for the welfare of the citizens. One might wonder what are the limits of entitlement to social control. Indeed, it may be argued that pure observers are not entitled to criticise the behaviours of society's members[6]. A reasonable boundary is represented by the edge of the range of influence or strength of the authority (whether legal or social) that generates the norm. An observer of a religious sect cannot criticise the behaviours of its members with regard to the prescriptions of their religious authority. However, she can criticise their behaviours with regard to a shared institutional authority, even when such behaviour is not expected from her. As an atheist I am not entitled to check whether a catholic goes to church. But I am entitled to control whether he respects the precept of tolerance in force in our shared society with regard to other religions even if I am not affiliated to any.

One important dimension to take into account is the type of norms under consideration. Classifications of norms abound not only in the philosophy of law but also in social psychology (Cialdini et al. 1991). Here, it is proposed to make a distinction between descriptive, norms and personal norms. For reasons discussed elsewhere (see Conte & Castelfranchi 1999), we maintain that norms are prescriptions (whether legal or social), which might refer to a single agent, although generally they concern a multi agent system. In several cases, a norm may be posed by a given agent upon herself (as happens in commitment). Usually, this implies that a given meta-norm is (perceived by the agent to be) called into question and leads to the formation or explicit issuing of a specific norm. A norm is always a prescription more or less explicitly posed on a non-empty set of agents.

Local motivations

Why do agents check whether norms are complied with? There seem to be several types of motivation arising from the circumstances described above. The reader is warned against considering these motivations as mutually exclusive, since probably in natural agents they appear in various combinations:

Mental requirements

The motivations identified above require agents to be endowed with some further mental properties, namely

Consequences of norm-based control

The consequences of social control at the group level are largely the same as discussed for social monitoring. The main difference is that the increased coherence of the group is based on a shared set of norms. Having explicit norms makes it easier to identify the similarities in behaviour for members of the group and also to identify the differences between the group and other groups.

A second possibility with norm-based control is to delegate the monitoring of certain norms to an individual member of the group.

At the individual level the consequences are more or less the same as already mentioned with social monitoring. The main new feature is that the possibility of social control will also lead some agents to exert normative influence on other agents.

* Norm influence

Sometimes, agents decide to increase the probability that agents subject to a given norm will actually comply with it. Influence is therefore based upon control: agents will put pressure on others to comply with the norms or with a given norm, when they have reasons to believe that the norm efficacy does not exceed the confidence level. Again, there is no need for x to be a member of the set of agent Yi upon which the norm is in force, but x must be a member of the set Y, to which Yi belongs.

Norm-based influence is here defined as the normative goal that the norm be accepted and applied by Yi. More precisely, x exercises a norm-based influence on Y when:

Motivations and requirements

Interestingly, even if x belongs to the subset Yi, there is no need for x to be an observer of the norm. In fact, x may resort to normative influence whenever this is convenient for her, for example, when x is a direct or indirect beneficiary of the norm. For example, it is far from rare that people complaining about bad drivers' habits when they are pedestrians, exhibit exactly the same bad behaviour when driving their cars. The motivations are essentially a subset of those that we have examined with regard to control. Analogously, the mental requirements[7] are a subset of the previous ones about norm-based control.


The more the agents are sensible to intra-group influence, the more norm-influence is expected to lead to an increase of norm efficacy, for two distinct reasons:

When, to what extent and in which modality is norm-based influence effective? And since norm efficacy, as was shown earlier in this paper, has an impact on agents' normative beliefs, what is the effect of norm efficacy on norm-based influence? Computer simulations could be designed to check the interactive effect of norm-based influence and beliefs about norm efficacy on the propagation of different types of norms and conventions in multi-agent systems.

* The necessity of more complex agents

Social monitoring, as discussed in this paper, is a rather complex social capacity of intelligent autonomous agents. It requires a more complex agent model and architecture than is usually implemented. What are the advantageous of such a view of social monitoring, which cannot be obtained by means of simpler instruments, for example with adaptive and learning systems?

This is not the forum for re-examining available adaptive learning systems. However, we essentially aim to draw the readers' attention to two complementary sides of social monitoring, imitation and social control. While imitation is a very popular notion (although often oversimplified and poorly or inadequately treated), the complementary aspect of social control is usually ignored or under-estimated within the computational fields of science. Indeed, it is considered as a puzzle within other fields, such as sociology. Our claim is that imitation and social control are interrelated aspects of one common phenomenon, i.e. social monitoring. By understanding this common ground, it is possible to see that agents check others' behaviours on the grounds of pre-existing internal criteria (for example, norms). Consequently, the emergence of social norms and standards, far from resulting from mere mutual adaptation, is a complex social and cognitive process. In this process, agents compare their own criteria with those of others, and extract from others' behaviours cues allowing them to specify abstract normative precepts.

What is the advantage of modelling such complex process, over the simpler, behavioural approach to social learning? There are several advantages:

Essentially, all these results imply the capacity to compare others' behaviours to one's own internal criteria. None of them can be achieved if agents were enabled to simply adapt to one another's behaviours.

* Research agenda

In this paper, we have formulated the requisites of a model of social monitoring. A great deal of work needs to be done in order to translate such a model into an agent platform, and implement it in order to obtain results in some relevant domain. Mainly, urgent developments should concern

* Conclusions

To understand how agents should be modelled in order to act in a multi-agent system (or society), we must understand the social rules that govern the behaviour of the individual agents. The social rules should be such that they strike a balance between the individual autonomy of the agents and the effective functioning of the complete society. In human societies, the social rules are formed through a complex set of social norms and conventions. In order to know which parts of this complex social system are of use for multi-agent systems we have to study the concepts which are important for human societies.

In this paper we have sketched a first analysis of the notions of social monitoring and control. We have put a particular emphasis on norm-based monitoring as we think that it is of prime importance for multi-agent systems as well.

In 4.9 we touched upon the notion of responsibility. We indicated why this notion is central for any complete account of social control and influence. A formal account of this notion is left for future work, which will also form a basis for a more formal description of the processes of social monitoring and control.

Future studies should be aimed at:

* Acknowledgements

We acknowledge with thanks the contributions of the European Union Framework Projects ALFEBIITE and FIRMA.

* Notes

1 Throughout the paper, we will speak of 'agents' to refer both to artificial, simulated agents, and to natural, real-world systems. The term 'actor', used by social scientists to refer to social systems especially at the individual level, evokes the script-based view of social action, which is not consistent with the conceptualisation of the present authors.

2 Note that by normative agents we do not refer to a characteristic of personality. In our meaning, a normative agent does not necessarily observe the norms, but is able to decide whether to do so.

3 In terms of (1) language and formalisms used (strictly logic-based in the logical-philosophical domain and more oriented to implementation languages in the (multi-)agent domain); (2) theory of reference (philosophy of law and deontic philosophy in the former domain as opposed to agent theory, game theory, AI in the latter); (3) objectives (expert legal systems, theory of institutions, in the former case as opposed to social theory and optimisation of coordination and cooperation in the latter) (cf. Conte et al., 1999).

4 This is shown by the example of looking for a shelter from rain. By observing what happens to another agent, the observer learns to avoid trees, where a negative event (lightning) is likely to occur. Here, the observer learns a negative effect of a known plan of action.

5 A caveat is necessary. What are the relationships, the connections among social monitoring and other related phenomena, for example, social comparison, learning, task execution monitoring? Social monitoring represents a means that under different conditions (goals and beliefs) may lead to different outputs. For example, a perceived discrepancy of powers may activate the mechanisms, rules and principles associated with social comparison (the equity principle), or may lead to updating the set of beliefs concerning one's position in the social hierarchy. A perceived discrepancy of behaviours may activate social learning. Given our main interest in the issue of norms spreading and the role of normative intelligence in such a phenomenon, we will examine these questions considering the contributions that a model of social monitoring can provide to clarify the issue of social control.

6 We are indebted to an anonymous reviewer of a draft of this paper for this interesting remark.

7 An interesting question here is what x can do to achieve normative influence. At least three modalities occur:

Further analysis should be carried out in order to strengthen and enrich this typology. Moreover, computer simulations of normative agents in interaction could be designed to check the respective effects of these modalities.

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