Abstract
How can reliability be generated and sustained in the face of uncertainty? This question is explored by examining knowledge networks among pastoralists and others in northern Kenya, emerging in response to a highly variable animal disease setting. Using quantitative and qualitative social network analysis, intersecting locally-embedded, development project and political networks are identified. Drawing on high-reliability theory, as applied to critical infrastructures, the paper explores the key characteristics of the knowledge networks in relation to systems, knowledges, relationships, technologies, professionals and politics. Reliability – the ability to provide stable services and respond variability in real-time – is shown to be related to the networked capacity to mobilise knowledge to confront uncertainty and avoid ignorance, with certain high-reliability professionals central. The locally-embedded network in particular has important characteristics of a high reliability knowledge network, but key brokers link to the development project and political network. Development challenges often require addressing uncertainty and even ignorance and lessons from high-reliability approaches can be crucial.
Highlights
How to generate reliable outcomes when things are so uncertain? This is a question asked by many, whether around natural disasters, climate change or disease pandemics
We suggest a high reliability framing, and insights on knowledge networks are relevant to wider conversations around responding to a world dominated by uncertainties
In the locallyembedded network we identified the triad of the chilres, the Animal Health Assistant (AHA) and Community Animal Health Worker (CAHW) as the high reliability professionals, emerging from their relationships in the network
Summary
How to generate reliable outcomes when things are so uncertain? This is a question asked by many, whether around natural disasters, climate change or disease pandemics. We use the idea of ‘high reliability’ more commonly applied in organisational theory to ‘critical infrastructures’, such as power stations, electricity grids and air traffic control (Roe & Schulman, 2012; Schulman & Roe, 2016). These settings seek to avoid catastrophic failures and manage high input variability. These systems demonstrate the continued presence of uncertainty – where the probabilities of particular outcomes are unpredictable and unknown, and ignorance – where we don’t know what we don’t know (Scoones, 2019).
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