Participatory modeling in sustainability science allows scientists to take stakeholders’ interests, knowledge and values into account when designing model-based solutions to sustainability problems, by incorporating stakeholders in the model-building process. This improves the chance of generating socially robust knowledge and consensus on solutions. Part of what helps in this regard is that scientists, through involving stakeholders, limit their own values from influencing the outcome, thus achieving some level of value-neutrality. We argue that while it might achieve this to some extent, it comes at a cost to the reliability of the outcomes, which is ethically problematic.

Robustness of climate models is considered by philosophers of climate science to be a crucial issue in determining whether and to what extent the projections of the Earth’s future climate that models yield should be trusted—and in turn whether society should pursue policies to address mitigation of and adaptation to anthropogenic climate change. Parker (2011) and Lloyd (2009, 2015) have introduced influential accounts of robustness for climate models with seemingly conflicting conclusions. I argue that Parker and Lloyd are characterizing distinct notions of robustness and that confidence in the projections is warranted in virtue of confidence in the models.

It has been argued that climate modeling can be partially characterized as exhibiting ontic competitive pluralism (i.e., that models compete for truth in some sense). I argue that (1) because climate models are all of the same model-type, they are not ontic competitors; instead (2) they compete in terms of local skill. Counterintuitively, locally poor performing models sometimes yield epistemic benefits for scientists, as demonstrated by the emergent constraints literature.

Climate scientists frequently employ heavily idealized models. How should these models be interpreted? Some philosophers have promoted a possibilist interpretation, where climate models stand in for possible scenarios that could occur, but don't provide information about how probable those scenarios are. The present paper argues that possibilism is undermotivated, incompatible with successful practices in the science, and liable to present a less accurate than probabilistic alternatives. There are good arguments to be had about how to interpret climate models but our starting point should be that the models provide evidence relevant to the evaluation of hypotheses concerning the actual world.