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2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops

Reflection, collectives and adaptation: the role of models in the design of Collective Adaptive Systems Stuart Anderson School of Informatics University of Edinburgh Edinburgh, UK

Mark Hartswood Department of Computer Science Wolfson Building, Parks Road Oxford, UK

Models can be used to exert control over the operation of units in the CAS and access to the model can enable particular kinds of collaborative behaviour. Later we will see some examples of this. This note focusses on the extent to which such models support shared reflective activity amongst members of collectives and how this supports collective action. The use of reflection in workplaces as an aid to improving quality and performance is well documented and in the context of workgroups or teams there are also benefits relating to collaborative reflection [2]. Reflection is also being considered as part of the development of community resilience programmes to develop community capacity to respond to unanticipated events [3]. As we move towards a world of blended real/virtual experience, workplaces are the locus for HDA-CASs where monitoring and automation blend with human action to deliver services. In this context, we believe techniques for the design, operation and evolution of HDA CASs that take account of the form of embedded models may help create better working environments and contribute to the delivery of better quality services. Mackenzie [4] has emphasised the role of models in the formation of particular forms of collective. Particularly striking is his description of the construction of a hedge fund team as an economic actor whose behaviour is distinctively different from any individual in the team [5]. A key member of the hedge fund team is concerned with managing information flow, integration and automation. In this aspect the hedge fund behaves like a social computation where the machine component is evolving in order better to support the activity of the economic actor [6]. In his work on financial markets Mackenzie has pointed out the role of mathematical models in the formation of modern financial markets. He has also pointed out the extent to which collective action is coordinated by models. One of the, now strongly contested, triumphs of modern mathematical finance is the extent to which notions like the efficient market hypothesis and value at risk, have acted in a performative way to create modern financial markets where the actions of many are coordinated by a model. The Value at Risk (VAR) model is very deeply

Abstract—We report work in progress on the role of models in the formation and maintenance of collectives in Hybrid Diversity-Aware Collective Adaptive Systems (HDA-CASs). HDA-CASs utilize hybrid computations involving machines and humans operating in collectives in a way that manages and leverages the diversity of collectives and machine-based computation. Here we explore the role of models in helping to constitute particular collectives and how models help shape the response of the collective. It appears that models are a potentially critical resource in collecting, sharing and acting on data gathered from the operation of CASs. This points to the potential role for models in the design of HDA-CASs. In particular we are interested in how models provide a sense of identity for a collective and can provide resources that shape the potential for collective action. Keywords-collective adaptive systems, reflection, models

Collective Adaptive Systems [1] is a broad class of systems that comprise many units, that have their own individual properties, objectives and actions. Decision-making is distributed and possibly highly dispersed, and interaction between the units may lead to the emergence of unanticipated phenomena. They are open, in that units may enter or leave collectives at any time, and boundaries between CASs are fluid and potentially overlapping. The units can be highly heterogeneous (computers, robots, agents, devices, biological entities, etc), each operating at different temporal and spatial scales, and having different (potentially conflicting) objectives and goals1 . Hybrid Diversity-Aware CASs are the focus for this paper and particularly aspects of hybridity. Hybridity is the capacity of CASs that involve humans to undertake social computations that involve tightly coordinated human and machine actions that leverage the strengths of the participants. Here we consider the emergence and support for human collectives that undertake actions in a coordinated manner. The particular form of support we consider here is the use of models to build and visualise observations of the world and the performance of the CAS in the world. Many CASlike systems have such models incorporated in their design. 1 This description is adapted from the EU FoCAS web page: http://focas. eu/about-focas/

978-1-4799-6378-2/14 $31.00 © 2014 IEEE DOI 10.1109/SASOW.2014.39

Marina Jirotka Department of Computer Science Wolfson Building, Parks Road Oxford, UK


embedded in trading systems to the extent that traders exceeding VAR thresholds are forced to adjust their trading positions to limit VAR. Thus the notion of VAR that is built on the broader theoretical base of Mathematical Finance has a very real impact on the behaviour of traders and markets and can act to coordinate the actions of large numbers of traders to force considerable volatility into markets.

I. O BSERVATIONS In any HDA-CAS deployment we can assume that some observations are being made of the behaviour of the CAS. Observations can be complex, we assume they will almost always take the form of a collection of time series for particular features of the CAS. The time series may each have different characteristics. For example they may differ in the frequency of observation and how accurate the observation is of a particular phenomenon. In what follows we will use the convention of using the letter h (decorated in various ways) to range over observations. We think of h as the history of the CAS. In any axiomatisation of a particular setting there will be some mapping from observations to the concrete setting. Here we don’t attempt much in the way of a formal analysis of the role of models in HDA CASs that is being developed in a companion piece to this paper. In any real world setting negotiating what are acceptable and reasonable collections of observations to make on a CAS can be sensitive and difficult since observations can often permit breaches of privacy and have the potential to provoke labour disputes over the monitory of work, efficiency and comparison between individuals. We will not consider this much here but in the context of our example it is clear that there is potential for such issues to arise and they have the potential to impair the effectiveness of a CAS approach. In the care home setting we assume that each badge records a single timeline at some resolution, say t is the time interval for that resolution so the observation of the badge is a series of sets of badge identifiers S1 , ...., Sn where, if the use of the badge started at time v, Si is the set of badge identifiers observed to be proximate to the badge at time v+t(i−1). The collection of all these badge timeseries is the observation we make of the care home setting. We probably cannot assume that all the badges are synchronised but if the time series are sufficiently fine grained this is probably not too much of a problem (in practice, repairing issues like this can be time consuming and difficult). Such observations contain quite a bit of data that allows verification since usually more than one badge makes an observation so we can check for consistency. It may also be the case that badges miss data sometimes so in general we will see incomplete and inconsistent data. Repairing and curating the datasets may in the long run be an important feature of the system but for the moment we will not consider how to manage this. In particular it may be possible to use social curation to rework timeseries and add annotations.

Mackenzie’s work prompted us to speculate on the extent to which models that allow collectives to reflect on their action and reason about the connection between their action and effects in the world might be relevant to the design of HDA CASs. This began from consideration of mechanisms that allow a group to act collectively. One potential category of mechanisms are those that allow members of a group to develop a sense of identity with other group members in some, possibly limited, contexts. In considering such mechanisms we identified shared models of the behaviour of the system might allow group members to reflect on how acting together they can achieve effects that could not be achieved by acting independently. This work is at a preliminary stage and we do not attempt to provide a general framework. Instead, we attempt to illustrate our ideas in a simple setting loosely based on some work done by the MIRROR project2 . The example setting we consider here is a care home with a large number of residents and associated care workers. Each of the residents and care workers have active badges that detect when badges are proximate to one another. The precise details of the observations made by the badges will depend on the technology deployed and the practices surrounding the deployed badges. The idea of this observation is that proximity is being used as a proxy for social interaction — when someone is close to another person it is likely that they are interacting in some way. Using proximity as an indicator of social interaction is certainly criticisable on a number of grounds. For example, there are a range of potential strategies that “game” the technology (leaving badges close to a group of residents, one person wearing multiple badges for a while, corralling residents so it is possible to be proximate to several at once, . . . ) and it may also miss some forms of positive interaction that do not require proximity. Such strategies are usually only adopted if they are incentivised by poor management. In the remainder of this note we consider how different models of aggregation of the data and different access to the data shape the options for care workers and residents. This is a particularly form of model (financial models of Value at Risk are much more complex) but it is illustrative and is useful in settings where what just happened is a reasonably good predictor of what is about to happen.

II. M ODELS Often models are intended to be predictive in some sense. In some settings this could be quite an elaborate mechanism but often the, perhaps default, model will be that the long term average behaviour will not change much so what happens in the next month, if we don’t change anything, is best predicted by the average of what happened over the



last few months. Models will usually predict observations with the same structure as the past observation. Models predict future observations on the basis of past observations and the strategy deployed by the CAS. Of course there may be no single “strategy” as such, it is much more likely to arise from the interaction of a range of elements of the CAS. If we want to analyse strategies we may be required to develop a representation or codification of the intended behaviour of the CAS (e.g. the actors, resources and governance structure for the CAS). Any such codification would require some validation process to ensure it captures the strategy reasonably well. Here it is important to think about the representation of the CAS since adaptation is an intensional phenomenon. To adapt we attempt to change the representation or codification of the CAS and adaptive strategies are all described in terms of how to change the codification rather than the functionality of the CAS because it is too hard to characterise the functionality. It may also be the case that we do not need to capture the whole of the CAS strategy explicitly, partial representations and partial models may be sufficient. In the case of the care home example we are interested in predicting patterns of interaction between residents and care workers. This is the focus because we know that social interaction is critical to the wellbeing of care home residents. We are using proximity as a proxy, so our model might say something like the amount of proximity in the next month for each resident will be similar to their average for the past three months. We might elaborate this and say that the pattern of interaction across each care worker will be similar next month to whatever it was last month. There are many possible choices of how we might make abstractions of the proximity data. For privacy or other motivations we might want to aggregate the observations made by the badges. A particular choice of how we aggregate the data will shape what the model is useful for. For example just recording the aggregate amount of proximity over the previous months for each resident would allow different approaches to a model based on the aggregate proximity of each resident with each care worker. Since one of these is a refinement of the other we might think there are more possible ways of directing the CAS resources in the second representation of the history. There are many possible models for the care home setting that might be more or less acceptable to care workers and residents. For example, if aggregated proximity data for every resident was available to care workers, residents, and relatives there is clear potential for the care worker group to act as a collective in order to manage the amount of proximity each resident receives. In addition there is the possibility for the residents to play a monitoring role to ensure their needs are met. By contrast, if each care worker could only see their own data and no other data their strategy could only be to ensure even distribution across all residents. There are many other possible models and access patterns

to the models in this setting. Different choices of model have different potential to support collective action and in the next section we consider the potential for collectives with different capacities for action and different notions of identity. III. C OLLECTIVES Models and access to models may help shape the form of collectives in a CAS. Any well-designed system will be responsive to changes in circumstances. For example, if more people require service at peak times then the queue lengths will grow, or if funding agencies provide more money then a charity may be able to recruit more workers who follow the established work patterns in the organisation. The unique feature of a CAS is the capacity to respond by a change in strategy. Access to model data combined with reflection on model predictions provide the means to do what ifs to try to change strategy to respond to changing circumstances. This capacity to change strategy is what lies at the heart of adaptive behaviour in a CAS. The capacity to change strategy in order to to change the outcome of the model is a key structure that can provide a notion of identity in the collective, and provides support for collective action. To get traction on the nature of collectives we need to look more carefully at strategies: • One approach is to assume that strategies have structure and that collectives are associated with components in the strategy (e.g. sub-strategies). The process of adaptation would then be the replacement of one substrategy with a different one. So collectives might form around particular sub-strategies because people have to work together. This would need work both around what a structure might look like and how to get the resourcing model right because we would expect resourcing to be on demand. • There may also be approaches that commit to less in terms of structure in strategies. For example, we could imagine that strategies have an ordering relationship where one strategy is a sub-strategy of another. Collectives might arise around experimentation with different substrategies to see what gives the best outcomes or reduces demand on the CAS. • It may also be possible to capture resourcing conflicts in models in this situation we might see antagonism between different collectives because of contention over commonly held resources. This could be a starting point for work on CAS governance [7]. A more detailed account calls for more work on strategies. It may be that they are representations of social computations. This needs more work to devise an expressive enough notion for our needs in attempting to account for the formation of collectives in response to access to model information. For example, we might hypothesise that a collective emerges around the sense of identity that arises


way of deciding a lead worker approach to care. Looking at data from the residents badges might also be useful since that is more oriented to ensuring the individual is well cared for and it may be possible to review the patterns with the individuals and see what pattern meets with their approval • Letting residents see their data either individually or collectively may result in the development of residents collectives who have the potential to take up antagonistic positions to the care workers if they identify workers who don’t spend much time proximate to residents or manifest some other deficiency. We should be careful to differentiate collective action from adaptation. Monitoring proximity and using that monitoring to change the distribution of proximity can be achieved by a fixed strategy or it might be achieved by changing the strategy — one is adaptive the other isn’t. This brings out the idea that adaptivity is really an intensional feature.

from sharing a strategy that requires collective action to execute. SO in this case we might expect the strategy would include information on: • • •

The sorts of capacities required in the collective to allow the strategy to be executed. An account of how model data provides the means to reflect and evaluate the strategy. Criteria for identifying when he evaluation identifies the need for adaptation of the strategy.

In the care home example one can imagine factions developing around different ways of valuing the work of care workers. For example one can imagine an infection control faction whose focus is primarily the control of infection that might come into opposition with a resident empowerment group of careworkers who pay more attention to interaction with residents. These might both be available sub strategies but if one uses resources necessary to carry out the other strategy then we may find the collectives around the different strategies are in conflict. In the care home example, consider the following different approaches to models. •

IV. R EFLECTION Reflection is the process of incorporating some reasoning about the model of the process into the process. In the case of adaptivity this should involve reflecting on how well the current strategy is achieving desired changes in the predicted behaviour of the system and considering how the strategy should be adapted to take account of changed circumstances. We can illustrate this in the care home example by considering three examples: • (Non Adaptive) Suppose some new residents arrive and are finding it hard to fit in, it may be that initially they have low proximity scores, this is identified in the observations of the CAS and the strategy of consciously engaging with those with low proximities will help them adapt. This is effectively business as usual with no need to change strategy. • (Adaptive) Suppose the care home starts to accept people who do not speak any English then the strategy may need to adapt so that data on languages talked by staff is gathered and there is a mechanisms to ensure there is always a care worker with appropriate language skills in attendance. In adaptive strategies there will usually be explicit reference to the model of the CAS. So here the model might predict that new people would be rapidly incorporated - but in the case of a language barrier this is likely to persist longer and thus the observed behaviour would not match the predicted behaviour and would prompt adaptation. • (Adaptive) Some people may need less contact and so persistently come up with low proximity scores. Adapting the process so care workers talk to those people with persistent low scores and identify loners who don’t need as much contact and so will always be outliers in the distribution and accepting that as

Suppose the model records the average amount of time each care worker is proximate to each resident and uses this to predict the care workers’ pattern of activity for the following month and that each care worker sees only their own data. Then the strategies we can use is for all care workers to try to even out their proximity to residents or for care workers to agree a division of labour where they have people they focus on and use their personal data to try to ensure they are focussing on their group of residents. Here we see a focal point for the creation of a collective that adapts the strategy to attempt to improve the balance out the amount of attention given to each resident. These strategies might provide only a weak sense of identity in the group because there is little need for deep cooperation since mostly care workers are striving to even out the time they spend with people even if there is some cooperation required to partition residents into groups that are cared for by particular groups of individuals. Suppose the model just has the aggregate proximity recorded over the past few months for each resident but no breakdown into proximity with particular care workers then the same strategy in the preceding case is possible but it is open loop on whether someone is succeeding in concentrating on a particular group of residents. This provides an even weaker focus for a collective. All care workers see the matrix of residents by care worker proximity. Other changes in strategy might be possible for example using data to identify care workers that particular residents like to talk to. This might be a


provision that treat nursing work as an undifferentiated activity thereby failing to articulate the extent to which nursing involves professional specialisation [8]. A variety of consequences may follow, for example, an inequality may arise between sub-collectives who have direct influence over models (management perhaps) and subcollectives having less control (perhaps carers or relatives). This may give rise to underhand strategies, possibly covert forms of managerial control, and counter-strategies. There is also the potential for the development of forms of timemarking by staff. Control over the model also implies that the model may be constructed to provide different results to different sub-collectives. Again, there are overtones of control and threats to autonomy here, for example, as in the recent controversy over Facebook’s experimental manipulation of its user’s moods where the news feed was subtly manipulated to differ from the model presumed by the majority of facebook users. While these types of asymmetry can create the conditions for the emergence of unethical behaviour, the seemingly appealing option of full transparency is often not a tenable solution. This is because occlusion or opacity are often necessary for hybrid systems to actually function [9]. So along with the model-collective-strategy nexus additional layers of governance and oversight will typically be needed to illuminate the distortions that cannot be managed within the first-order HDA-CAS. A final point that draws together the above discussion concerns the embedding of social values within the HDACAS. Engineering disciplines risk conceiving HDA-CAS from a very instrumental, technicised perspective where it is all too easy to aim towards maximising contact time as a metric of good care, rather than orienting to quality of interactions and individual need. The complicated question is how to create HDA-CASs that leverage off data, but (in the care example) maintain the ends of delivering care that is personal, compassionate, empathic and responsive which are characteristics of the quality of interactions, not necessarily their frequency or duration.

something that does not need action effectively changes the strategy. This is an example of where we need to adapt the model from one where the model anticipates equal amounts of interaction as being the desirable state to one where there is a distribution of levels of interaction and the strategy is to determine the level of interaction each person requires and have the strategy of meeting those requirements. V. A DAPTING This amounts to somehow devising new strategies that result in some changes in CAS behaviour that moves away from the predicted behaviour closer to some new behaviour that is stable for the new strategy. So any adaptive mechanisms requires at least a way of generating alternative strategies to evaluate and some evaluation criteria that allow us to compare the future as predicted by the model for different strategies. It may be that this is an important aspect of hybridity because we depend on people to come up with new strategies rather than explicitly building a strategy generator while the model is computer-based. This form of adaptation is limited because it takes place inside the current model of the system. Adding observations to the model and changing the presentation, access and analytical tools within the model would involve greater reflection and the development of a proposal for radical change. VI. E THICAL C ONSIDERATIONS Separating out collectives, strategies and models from within HDA-CASs also helps us to think through a series of ethical issues that arise as HDA-CAS are formed and evolve. For example, different sub-collectives may be advantaged or disadvantaged depending upon the model or strategies that come to dominate within the HDA-CAS — giving the processes of selecting models or strategies a distinctly political complexion. Typical categories of design issues that can result in disadvantage for some collectives and advantages to others are: • Driving the model construction from a KPI structure that contains perverse incentives. Well known examples can be found in Jon Seddon’s book [?]. For example, the housing benefit systems that rewarded staff for processing large numbers of people but did not measure the time from first application to award of benefit for benefit claimants that encouraged staff to keep applicants waiting in the system for long periods of time. • Control of access, views, and reports from models to support a group’s perspective while failing to provide effective support to other groups. • Constructing reporting categories so that statistics in the model support a particular perspective on the system. For example, categories of care activity in health

VII. C ONCLUSION This work is at a very preliminary stage and the main reason for submitting it is to stimulate discussion within the community on the role of models in collective formulation. In particular, we believe that the design of models that allow reflection on the activity of the CAS will play an important role in the overall design of CASs. By considering the care home example, we can see that if the model restricts access to the data to individuals then there is little motivation for collective action supported by the system because there is no way to direct action to improve the working conditions of staff or the care received by residents. However, if we allow more collective access to data and provide overview data across all resident there is substantial scope for collective


action. This range of potential action needs also to be located within an ethical framework and analysing model construction and access provides us with tools to analyse the ethical consequence of the design and use of models in CASs

[10] T. Erickson and W. A. Kellogg, “Social translucence: An approach to designing systems that support social processes,” ACM Trans. Comput.-Hum. Interact., vol. 7, no. 1, pp. 59–83, Mar. 2000. [Online]. Available: http: //

ACKNOWLEDGMENT The research leading to these results has received partial funding from the European Community’s Seventh Framework Program (FP7/2007-2013) under grant agreement n. 600854 Smart Society: hybrid and diversity-aware collective adaptive systems: where people meet machines to build smarter societies ( R EFERENCES [1] S. Anderson, N. Bredeche, A. Eiben, G. Kampis, and M. van Steen, Adaptive Collective Systems. FoCAS, 2014. [2] M. Prilla, M. Degeling, and T. Herrmann, “Collaborative reflection at work: Supporting informal learning at a healthcare workplace,” in Proceedings of the 17th ACM International Conference on Supporting Group Work, ser. GROUP ’12. New York, NY, USA: ACM, 2012, pp. 55– 64. [Online]. Available: 2389185 [3] K. B. Wells, J. Tang, E. Lizaola, F. Jones, A. Brown, A. Stayton, M. Williams, A. Chandra, D. Eisenman, S. Fogleman, and A. Plough, “Applying community engagement to disaster planning: Developing the vision and design for the los angeles county community disaster resilience initiative,” American Journal of Public Health, vol. 103, no. 7, pp. 1172–1180, 2014/08/06 2013. [Online]. Available: [4] D. MacKenzie, An Engine, Not a Camera: How Financial Models Shape Markets, ser. Inside technology. MIT Press, 2006. [Online]. Available: id=BU7qngEACAAJ [5] I. Hardie and D. MacKenzie, “Assembling an economic actor: the agencement of a hedge fund,” The Sociological Review, vol. 55, no. 1, pp. 57–80, 2007. [6] D. Robertson and F. Giunchiglia, “Programming the social computer,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 371, no. 1987, 2013. [Online]. Available: 1987/20120379.abstract [7] E. Ostrom, “Polycentric systems as one approach for solving collective-action problems,” Available at SSRN 1304697, 2008. [8] J. Seddon, Systems Thinking in the Public Sector: The failure of the reform regime and a manifesto for a better way. Triarchy Press, 2008. [9] G. C. Bowker and S. L. Star, Sorting things out: Classification and its consequences. MIT press, 1999.


"Reflection, Collectives and Adaptation: the Role of Models in the Design of Coll. Adaptive Systems  
"Reflection, Collectives and Adaptation: the Role of Models in the Design of Coll. Adaptive Systems