Samhar Mahmoud

Efficient norm emergence through experiential dynamic punishment

Peer punishment has been an effective means to ensure that norms are complied with in a population of self-interested agents. However, current approaches to establishing norms have only considered static punishments, which do not vary with the magnitude or frequency of norm violation. Such static punishments are difficult to apply because it is difficult to identify an appropriate fixed penalty: one that is not too weak to disincentivise norm violations and not too strong to lead to significant deleterious effects on the system as a whole (such as those incurred by losing the benefits of a member of the population). This paper addresses this concern by developing an adaptive punishment technique that tailors penalty to norm violation. An experimental evaluation of the approach demonstrates its value compared to static punishment. In particular, the results show that our dynamic punishment technique is capable of achieving norm emergence, even when starting with an amount of punishment that is too low to achieve emergence in the traditional static approach.

Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation

Background Observational and experimental studies of the diagnostic task have demonstrated the importance of the first hypotheses that come to mind for accurate diagnosis. A prototype decision support system (DSS) designed to support GPs’ first impressions has been integrated with a commercial electronic health record (EHR) system. Aim To evaluate the prototype DSS in a high-fidelity simulation. Design and setting Within-participant design: 34 GPs consulted with six standardised patients (actors) using their usual EHR. On a different day, GPs used the EHR with the integrated DSS to consult with six other patients, matched for difficulty and counterbalanced. Method Entering the reason for encounter triggered the DSS, which provided a patient-specific list of potential diagnoses, and supported coding of symptoms during the consultation. At each consultation, GPs recorded their diagnosis and management. At the end, they completed a usability questionnaire. The actors completed a satisfaction questionnaire after each consultation. Results There was an 8–9% absolute improvement in diagnostic accuracy when the DSS was used. This improvement was significant (odds ratio [OR] 1.41, 95% confidence interval [CI] = 1.13 to 1.77, P<0.01). There was no associated increase of investigations ordered or consultation length. GPs coded significantly more data when using the DSS (mean 12.35 with the DSS versus 1.64 without), and were generally satisfied with its usability. Patient satisfaction ratings were the same for consultations with and without the DSS. Conclusion The DSS prototype was successfully employed in simulated consultations of high fidelity, with no measurable influences on patient satisfaction. The substantially increased data coding can operate as motivation for future DSS adoption.

Establishing norms with metanorms in distributed computational systems

AbstractNorms provide a valuable mechanism for establishing coherent cooperative behaviour in decentralised systems in which there is no central authority. One of the most influential formulations of norm emergence was proposed by Axelrod (Am Political Sci Rev 80(4):1095–1111, 1986). This paper provides an empirical analysis of aspects of Axelrod’s approach, by exploring some of the key assumptions made in previous evaluations of the model. We explore the dynamics of norm emergence and the occurrence of norm collapse when applying the model over extended durations . It is this phenomenon of norm collapse that can motivate the emergence of a central authority to enforce laws and so preserve the norms, rather than relying on individuals to punish defection. Our findings identify characteristics that significantly influence norm establishment using Axelrod’s formulation, but are likely to be of importance for norm establishment more generally. Moreover, Axelrod’s model suffers from significant limitations in assuming that private strategies of individuals are available to others, and that agents are omniscient in being aware of all norm violations and punishments. Because this is an unreasonable expectation , the approach does not lend itself to modelling real-world systems such as online networks or electronic markets. In response, the paper proposes alternatives to Axelrod’s model, by replacing the evolutionary approach, enabling agents to learn, and by restricting the metapunishment of agents to cases where the original defection is observed, in order to be able to apply the model to real-world domains . This work can also help explain the formation of a “social contract” to legitimate enforcement by a central authority.

Overcoming Omniscience in Axelrod's Model

Norms are a valuable mechanism for establishing coherent cooperative behaviour in decentralised systems in which no central authority exists. In this context, Axelrodsseminal model of norm establishment in populations of self interested individuals [1] is important in providing insight into the mechanisms needed to support this. However, Axelrodsmodel suffers from significant limitations: it adopts an evolutionary approach, and assumes that information is available to all agents in the system. In particular, the model assumes that the private strategies of individuals are available to others, and that agents are omniscient in being aware of all norm violations and punishments. Because this is an unreasonable expectation, the approach does not lend itself to modelling real world systems such as peer-to-peer networks. In response, this paper proposes alternatives to Axelrods model, by replacing the evolutionary approach, enabling agents to learn, and by restricting the met punishment of agents to only those where the original defection is perceived, in order to be able to apply the model to real-world domains.

Norm emergence through dynamic policy adaptation in scale free networks

As has been stated elsewhere, norms are a valuable means of establishing coherent cooperative behaviour in decentralised systems in which there is no central authority. Axelrods seminal model of norm establishment in populations of self-interested individuals provides some insight into the mechanisms needed to support this through the use of metanorms, but considers only limited scenarios and domains. While further developments of Axelrods model have addressed some of the limitations, in particular in considering its application to different topological structures, this too has been limited in not offering an effective means of bringing about norm compliance in scale-free networks, due to the problematic effects of hubs. This paper offers a solution, first by adjusting the model to more appropriately reflect the characteristics of the problem, and second by offering a new dynamic policy adaptation approach to learning the right behaviour. Experimental results demonstrate that this dynamic policy adaptation overcomes the difficulties posed by asymmetric distribution of links in scale-free networks, leading to an absence of norm violation, and instead norm emergence.

Norm Establishment via Metanorms in Network Topologies

In order to established a norm in a society of agents, met norms have previously been proposed as a means of ensuring not that norms are complied with, but that they are enforced. Yet while experimental results have shown that metanorms are effective in fully-connected environments such as that used by Axel rod, there has been limited consideration of such metanorm models with different but more realistic topological configurations. In this paper, therefore, we consider the use of metanorms in supporting norm establishment in lattices and small world networks. Our results suggest that norm establishment is achievable in lattices and small worlds.

Establishing norms for network topologies

In order to establish a norm in a society of agents, metanorms have previously been proposed as a means of ensuring not that norms are complied with, but that they are enforced. Yet while experimental results have shown that metanorms are effective in fully-connected environments such as that used by Axelrod, there has been limited consideration of such metanorm models with different but more realistic topological configurations. In this paper, therefore, we consider the use of metanorms in supporting norm establishment in lattices and small world networks. Our results suggest that norm establishment is achievable in lattices and small worlds.

Simulation and NorMAS

In this chapter, we discuss state of the art and future perspective of the study of norms with simulative methodologies, in particular employing agent-based simulation. After presenting the state of the art and framing the simulative research on norms in a norm life-cycle schema, we list those research challenges that we feel more apt to be tackled by the simulative approach. We conclude the chapter with the indications for the realization of a NorMAS simulation platform, illustrated by selected scenarios.

Optimised Reputation-Based Adaptive Punishment for Limited Observability

The use of social norms has proven to be effective in the self-governance of decentralised systems in which there is no central authority. Axelrods seminal model of norm establishment in populations of self-interested individuals provides some insight into the mechanisms needed to support this through the use of metanorms, but is not directly applicable to real world scenarios such as online peer-to-peer communities, for example. In particular, it does not reflect different topological arrangements of interactions. While some recent efforts have sought to address these limitations, they are also limited in not considering the point-to-point interactions between agents that arise in real systems, but only interactions that are visible to an entire neighbourhood. The objective of this paper is twofold: firstly to incorporate these realistic adaptations to the original model, and secondly, to provide agents with reputation based mechanisms that allow them to dynamically optimise the intensity of punishment ensuring norm establishment in exactly these limited observation conditions.

Overcoming omniscience for norm emergence in Axelrod's metanorm model

Norms are a valuable mechanism for establishing coherent cooperative behaviour in decentralised systems in which no central authority exists. In this context, Axelrods seminal model of norm establishment in populations of self-interested individuals [2] is important in providing insight into the mechanisms needed to support this. However, Axelrods model suffers from significant limitations: it adopts an evolutionary approach, and assumes that information is available to all agents in the system. In particular, the model assumes that the private strategies of individuals are available to others, and that agents are omniscient in being aware of all norm violations and punishments. Because this is an unreasonable expectation, the approach does not lend itself to modelling real-world systems such as peer-to-peer networks. In response, this paper proposes alternatives to Axelrods model, by replacing the evolutionary approach, enabling agents to learn, and by restricting the metapunishment of agents to only those where the original defection is perceived, in order to be able to apply the model to real-world domains.