ClusterOS Diagnostic Profile
Tulsa Regional Innovation Ecosystem
Tulsa Regional Innovation Ecosystem exhibits 6 observable stalls with Coordinating instead of deciding and Scaling activity instead of throughput as primary behavioural patterns. 4 stabilisation stacks identified.
Coordinating behaviors (Coordinating: partnerships, regional alignment) may sustain activity scaling (Scaling activity: program expansion, participant growth) by creating venues for announcing initiatives and distributing participation opportunities. Activity scaling may sustain coordination by generating content for partnership formations and justifying continued alignment efforts. Both X-sides observable across...
Intermediary entities (Mediating: 36 Degrees North, coordination bodies) may sustain incumbent centrality (Stabilising: GKFF, higher ed, Fortune 500) by channeling connections through established actors rather than enabling direct coupling to emergent entities. Incumbent presence may sustain intermediation by providing stable anchor points for brokerage and legitimizing intermediary roles. Both X-sides...
Re-proving behaviors (Re-proving: multiple training programs, program portfolios) may sustain permission-seeking (Waiting: federal funding concentration) by generating diverse initiatives that require external validation and resources. Permission-seeking may sustain re-proving by providing funding for new program establishment rather than deepening existing commitments. Both X-sides observable across...
Coordinating behaviors (Coordinating: partnerships, regional alignment) may sustain intermediation structures (Mediating: 36 Degrees North, coordination entities) by creating demand for neutral convening capacity and brokerage services. Intermediation may sustain coordination by providing venues and processes for partnership formation. Both X-sides observable with significant entity overlap ( appears in both; 36...
"If partnership formation timing were distributed across longer intervals rather than concentrated in single periods, it might reduce the system's ability to absorb pressure for momentum demonstration without requiring substantive program...
Leverage hypotheses are testable perturbations, not prescriptions. Where demand-side behaviour is weakly visible, the correct move is observation — improving visibility before attempting change.
A full ClusterOS diagnostic adds actor questionnaire data, working sessions, and anchor interviews — producing higher-confidence stall identification, board-ready stack analysis, and leverage hypotheses calibrated to your specific context.