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The culture trap is the tendency to put cultural markers and habits above the demands of reason or compassion. It can reduce receptivity to new ideas and trigger Phoenix Cycles of catastrophe and renaissance. System research is then complicated by the historiographic problem of continuity and change, because there are no objective criteria for deciding whether "the system" survived or was destroyed by the catastrophe. This paper explores the differences between uncertainty emergence and self-organizing emergence using the concept of a "possibility space" to clarify the relationship between anti-causal events and causal states, i.e., the meso-history of conjuncture. Conjunctures are interpreted ex post in the context of deep time. The paper distinguishes autopoiesis, i.e., a new instance of a species, from elaboration, i.e., the origin of new species. It also describes a spectrum ranging from metastability, in which the possibility space is time invariant, to innovation, in which the possibility space is a synergetic artefact of changing habits and beliefs. Metastable systems are computably complex; we can make predictions across the boundaries of successive conjunctures, subject to statistical uncertainties. Innovative systems are uncomputably complex; although we can make predictions within a conjuncture and anticipate bottlenecks, we cannot predict beyond the conjuncture because conceptual taxonomies will not be conserved. The relationship between adaptive potential and constraint remains fundamental, although debates about whether the system is resilient are meaningless. System boundaries are artefacts of a dynamic consensus. The analytical sciences use methods designed for metastable systems, whereas the discursive sciences presume innovation. These methods predispose researchers to different space-time perspectives that, in turn, make it possible to generalize about their roles in different policy arenas. Tensions between analytical and discursive scientists become more understandable and manageable if these historiographic issues are clarified. The relationship between innovation and metastability is illustrated in an appendix on the emergence of social complexity in Europe. [ABSTRACT FROM AUTHOR]

The ability to incorporate and manage the different drivers of land-use change in a modeling process is one of the key challenges because they are complex and are both quantitative and qualitative in nature. This paper uses Bayesian belief networks (BBN) to incorporate characteristics of land managers in the modeling process and to enhance our understanding of land-use change based on the limited and disparate sources of information. One of the two models based on spatial data represented land managers in the form of a quantitative variable, the area of individual holdings, whereas the other model included qualitative data from a survey of land managers. Random samples from the spatial data provided evidence of the relationship between the different variables, which I used to develop the BBN structure. The model was tested for four different posterior probability distributions, and results showed that the trained and learned models are better at predicting land use than the uniform and random models. The inference from the model demonstrated the constraints that biophysical characteristics impose on land managers; for older land managers without heirs, there is a higher probability of the land use being arable agriculture. The results show the benefits of incorporating a more complex notion of land managers in land-use models, and of using different empirical data sources in the modeling process. Future research should focus on incorporating more complex social processes into the modeling structure, as well as incorporating spatio-temporal dynamics in a BBN. [ABSTRACT FROM AUTHOR]