Three different types of versions of ParEvo have been conceived, and some of them actually implemented
- Face to Face ParEvo – suitable to classrooms and workshops. Described in detail on this webpage
- Scaled Up ParEvo – enabling a much larger group of people to be involved
- Gamified ParEvo – providing participants with feedback on their relative performance
- Articulating a Theory of Change – changing the parameters of a ParEvo to make this possible
2. Scaled Up ParEvo
- .Observers/Audiences: The basic online version of ParEvo does not involve any kind of audience. There is just a a Facilitator and the invited participants. But in 2021 Eva Otero used ParEvo to evaluate a UN volunteer sending programme. 12 participants described the (imagined) experiences of new volunteers, over eight iterations. The completed storylines were then shared with a large audience of actual UN volunteers, who were asked a number of questions via an online survey, including how realistic the different storylines were, in the light of their own experiences.
- Commentators: These are people invited to comment on the contributions made in each iteration, before progress onto the next iteration. They are usually small in number and have some relevant subject knowledge.
- Commentators can also channel/filter responses by a wide population of people i.e. the audience
- The Facilitator can be the person making comments after each iteration, and their responses can be informed by a larger team of Observers, as was the case with an exercise in May 2022
- Teams as participants: Individual participants each represent the views of different teams of people. Members of each team interact with each other during each iteration (after reading the most recent contributions by other teams and when planning their own new contribution). There is evidence that this kind of modularity can improve “collective intelligence” types performance. See Navajas, J., Niella, T., Garbulsky, G., Bahrami, B., & Sigman, M. (2018). Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds. Nature Human Behaviour, 2(2), 126–132 “Remarkably, combining as few as four [team] consensus choices outperformed the wisdom of thousands of individuals“
- First Come First Served: In this version the number of approved participants will be much larger than the number allowed to actually contribute in each iteration. In each iteration contributions will be accepted on a first come first accepted basis, until a predefined maximum allowable number of contributions is received.
- Double Darwinian: In the basic version of ParEvo participants are only allowed to extend storylines that were added to in the previous iteration, not those that were ignored. In this version there is an additional rule. Participants whose contributions were not built on by others in the previous iteration drop out of the pool of active participants, and are placed at the bottom of a waiting list. They are replaced by another at the top of a waiting list.
- Leader board: Participants are given scores which represent the total number of all contributions others have added to their contributions, and these scores are made visible to all participants. The scoring system excludes contributions which built on one’s own previous contributions. This option is now available, and can be switched on/off by the facilitator at any stage of a ParEvo exercise.
4. Articulating a Theory of Change
Background: A Theory of Change is a detailed story of how a person organisation seeks to achieve one or more objectives through carrying out various activities. Ideally it will be invaluable i.e. it will be possible to identify if and when all the various activities have been carried out as required and when the objective has been adequately achieved. There is a change are widely used in development aid programs as well as policy initiatives within developed economies.
At first glance, ParEvo could easily be used to help group of people to articulate how they or others could achieve an objective. But this would depend on the parameters i.e. the particular settings of the ParEvo exercise. In most ParEvo exercises to date participants have been asked to write down what they think could happen next. What they do end up writing usually very substantially in terms of likelihood and desirability. But the content of a theory of change is typically more restricted in focus, concentrating on what the participant thinks would be a desirable sequence of events and which presumably which they think is likely to occur.
If ParEvo is to be used to help people articulate their theory of change, in narrative form in the first instance, then the guidance given to the participants needs to be changed. There are three options which could be considered, each of which may be appropriate in different circumstances:
- Participants are asked to write about events which are likely to happen, which could include both desirable and undesirable development.
- Participants are asked to write about events which are desirable, which could include both likely and unlikely developments.
- Participants are asked to write about events which are both desirable and likely.
After a set of storylines has been developed within one of these new types of guidance there will be a second challenge. This will be to convert the narrative text, which could be as long as 1000 to 1500 words, into more summary diagrammatic form. This will typically show various events with causal connections between. For a discussion of the range of options that have been used in the past, along with some of the limitations, see Davies 2017.
One possible way forward would be to identify all the types of actors in the narrative and to place these as “nodes” within the diagram, and then create links between particular actor nodes where the narrative talks about a significant causal influence taking place between one actor and the other. This actor centred approach seems likely to be easier to do than one that tries to document the relationship between more abstract/disembodied causal factors, which will be more difficult to identify from the storyline narrative.