The evolution of organismality is a social process.All organisms originated from groups of simpler units that now show high cooperation among the parts and are nearly free of conflicts. We suggest that this near-unanimous cooperation be taken as the defining trait of organisms. Consistency then requires that we accept some unconventional organisms, including some social insect colonies, some microbial groups and viruses, a few sexual partnerships and a number of mutualistic associations.
Whether we call these organisms or not, a major task is to explain such cooperative entities, and our survey suggests that many of the traits commonly used to define organisms are not essential.
These non-essential traits include physical contiguity, indivisibility, clonality or high relatedness, development from a single cell, short-term and long-term genetic cotransmission, germ–soma separation and membership in the same species.
(Source: rstb.royalsocietypublishing.org)
The overarching function of science operating in the “web” of innovation is not just to generate knowledge, but to relate what is known and what is unknown in complex situations of change. Of course, science as an agent of change will generate new knowledge—this is what research is all about. But on the road to that knowledge it will also accumulate new uncertainties and open questions. And the non-knowledge generated may well outstrip the relevant knowledge.
In model terms, in zones of sensitivity or crisis situations, we find fractal borders between basins of attraction, so that any move, no matter how small and in no matter what any direction, might – or might not – trigger the move to another basin of attraction. Here we have an irreducible element of ‘chance’ even though the system is thoroughly deterministic.
As we have said, what keeps a system inside a behaviour pattern – represented by the trajectories inhabiting a basin of attraction – is the operation of negative feedback loops that respond to system fluctuations below a certain threshold of recuperation by quickly returning to the system to its pattern. With regard to normal functioning, fluctuations are mere ‘perturbations’ to be corrected for in a stable system. Since internal system resources translate the sense of events into terms significant to that system, external events are merely ‘triggers’: they trigger a pre-patterned response. Such changes in environment relevant to the system’s ‘interests’ are called ‘signs’.
Now fluctuations of a certain magnitude – beyond the recuperative power of the negative feedback loops or homeostatic mechanisms – will push the system past a threshold, perhaps to another pattern in its fixed repertoire, or perhaps into a ‘death zone’ where there are no patterns but only static or chaos. Thus some stable systems are ‘brittle’: they can be broken and die.
Some systems are ‘resilient’ however: a sign or trigger that provokes a response that overwhelms its stereotyped defensive patterns and pushes the system beyond the thresholds of its comfort zones will not result in death but in the creation of new attractors representing new behaviours. We call this ‘learning’. (Although of course there is a sense in which the old system has died and the new one is ‘born again’. All sorts of question of personal identity could be raised here.)
Sometimes this learning, this creation of new patterns for a particular system, repeats patterns typical of systems of its kind; we call this ‘development’. Sometimes however this learning is truly creation: we call this ‘evolution’, or as we will see, ‘diachronic emergence’. Diachronic emergence, or creativity in the production of new patterns and thresholds of behaviour, is what Deleuze will call an ‘event’, which is not to be confused with a mere switch between already established patterns or with the trigger or ‘external event’ that pushes the system past a threshold and produces the switch.
An event repatterns the system.
Epistemologies drive presumptions about the “relationship between the researcher and the system/object of study and modeling of system processes” (MacMynowski 2007). They shape how researchers answer questions regarding the validity of knowledge (qualitative vs. quantitative, etc.), the legitimacy of methods to produce knowledge (experimentation, induction, hypothesis testing, etc.), and the assumptions inherent in particular conceptualizations of the object of study and certain methodologies.
We focus on epistemological pluralism and an iterative process of negotiating values, epistemologies, and knowledge using resilience theory’s adaptive cycle as a conceptual framework.
we demonstrate how well-intentioned efforts toward interdisciplinary research have served to privilege one epistemology over another in question formulation and research, and how a reorganization based on epistemological pluralism might lead to the production of more fully integrated knowledge.
A given scientific community does not just want knowledge, but knowledge about a particular set of things. Such cognitive aims are not decided upon by the individual; rather, they are negotiated within a set of unquestioned social institutions (rules) that are underpinned with ideological perspectives, such as a discipline.
Despite progress in interdisciplinary research, many efforts are hampered by a host of problems, including a tendency to privilege a single epistemological and disciplinary perspective. Different disciplines carry with them different epistemologies, or theories of knowledge. That is, each may have a different conception of what constitutes knowledge, how it is produced, and how it should be applied (Rescher 2003). The privileging of a single disciplinary or single epistemological perspective limits the potential variety of scientific and local knowledge that can contribute to our understanding.