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Amitaksha Nag

United States of America

Amitaksha Nag Member since 03/12/2024

Independent Consultant

Website

I specialize in facilitating collective learning of complex systems to enhance public participation in policy decisions and drive systemic transformation. My work integrates systems thinking, system dynamics modeling, social network analysis, and iterative, adaptive methods for problem diagnostics, program design, and evaluation, all focused on fostering systems change.

For over 20 years, I have advised organizations including the World Bank, IFC, MCC, USAID, UNICEF, CGAIR, GIZ and numerous programs across the Global South, leveraging systems change approaches to build capacities for scaling development interventions.

My recent advisory work includes supporting systems change adoption in eight country programs with the Millennium Challenge Corporation, where I applied systems approaches for root-cause analysis, high-leverage solutions, and Theory-of-Change development, alongside adaptive methods to engage country-level stakeholders. For the World Bank, I conducted systems analysis in Madagascar to identify the root causes of poverty, revealing multidimensional feedback mechanisms and pathways for poverty alleviation. In implementing the CGIAR Transformation of Agrifood systems in South Asia (TAFSSA) initiative, I applied social network analysis to identify opportunities to strengthen technical collaborations and enhance the adoption of agrifood and nutrition research in India and Bangladesh. For UNICEF-LAC, I developed a partnership mapping framework and toolkit to build networks and scale programs across Latin America and the Caribbean.

Currently, I serve as a Subject Matter Expert with CGIAR's Independent Advisory and Evaluation Service (IAES), focusing on innovation and scaling impact.

My contributions

  • Discussion

    Evaluating Scaling Efforts: Measuring What Matters

    • Amitaksha Nag

      United States of America

      Amitaksha Nag

      Independent Consultant

      Posted on 18/12/2024

      Hello everyone,

      Reflecting on my experience, my work primarily focused on three key strands of evaluation for innovation and scaling:

      1. Evaluating Systems
      • Scaling requires clear benchmarks of success that reflect specific needs and contexts. For example, localized interventions may not apply universally, and some innovations might become obsolete or counterproductive over time.
      • Given the multidimensional nature of scaling—spanning socio-economic, ecological, and cultural aspects—frameworks must align strategies across diverse contexts and components.
      • Assessment of the value of scaling efforts by measuring potential returns, costs, and unintended consequences.
      • Diffusion mechanics, such as adoption patterns modeled through tools like the Bass model, helps to understand the trajectory from early adoption and growth to eventual saturation.
      • Scenario-based risk assessments and simulations are useful to identify conditions for success, anticipate challenges, and refine strategies for scaling.
      1. Evaluating Networks
      • Understanding stakeholder ecosystem—whether tightly knit or dispersed—can help identify early adopters and drive broader scaling strategy. 
      • Addressing diverse interests and agendas across stakeholders, such as government, private sector, and local leaders, is crucial to align incentives and secure buy-in. 
      • Mitigating resistance from those benefiting from the status quo is essential to overcome barriers. 
      • Exploring co-creation opportunities, such as collaborative funding, strategic alliances, and public-private partnerships
      1. Evaluating Adaptability
      • Embedding evaluation within adaptive approaches fosters continuous learning through iterative design, testing, adaptation, and feedback loops executed in short cycles.
      • Tools like the Rapid Results Approach (RRA) use structured processes to enhance focus and collaboration among delivery teams, with evaluation establishing performance metrics to drive results iteratively.
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