Pain expressiveness and altruistic behavior

Predictions which invoke evolutionary mechanisms ar e hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interac tions in specific physical or social environments o ver many generations. The outcomes have implications fo r understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/non-expresser strategies) when injured, and of helping, ignoring or exploiting another in pain (altruistic/non-altruistic/selfish strategies) . Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury inte rrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, conti ngencies evident from experimental work with a variety of mammals, over a few interactions, were r eplicated in the agent-based model after selection pressure over many generations. More energy-demandi ng expression of pain reduced its frequency in successive generations, and increasing injury frequ ency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased e xpression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits o f helping hardly changed its frequency, while increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent- based modeling allows simulation of complex behavio urs and environmental pressures over evolutionary time.

Supervision For School Staff: What is Valuable about Solution Circles?

Group supervision is used for support, education and/or monitoring. Despite the potential value of these elements for school staff, it is rarely practised. This mixed methods research, from a critical realist perspective, explored the use of Solution Circles to structure staff supervision groups in three schools. Five circles were run in each school, involving thirty-one participants, eighteen of whom contributed data. Thirteen staff trained as facilitators. The self-efficacy, resilience and anxiety levels of the staff taking part were not found to be significantly different as a result of the intervention. However, a small effect size was noted for self-efficacy, perhaps worthy of further investigation in the context of the small sample size. Thematic analysis of participant feedback (gathered during the last circle, which ran as a Focus Group) indicated the following mechanisms as affecting the value of Solution Circles for staff supervision groups: the structure of the sessions; aspects linked to the groups meeting a ‘need to talk’; elements which helped participants to ‘feel like a team’; and, school context factors. Semi-structured interview data from six facilitators indicated that the structure of the circles, individual characteristics of facilitators, the provision of support for facilitators, and elements of the wider school context, were all mechanisms which affected the facilitation of the programme. Further research might implement elements of these mechanisms and measure their impact.