Computational modeling frameworks for Kampala (CAADP) diagnostics: Prof. Christian Henning

Prof. Christian Henning, Chief Scientist, AKADEMIYA2063

Transcript

1. Prof. Christian Henning Chief Scientist, AKADEMIYA2063
2. Computational Modeling and Analytical Tools in Applied Policy Analysis I. Motivation II. Outline of Computational framework
3. The role of modeling in policy analysis ➢Understanding which policies work best to solve sustainable development problems in specific countries is nowadays still both challenging and crucial. ➢ Beyond generating scientific knowledge on policy impact a second and may be even bigger challenge is to integrate this knowledge into real world politics. ➢ A crucial role in understanding the impact of polcies plays economic modeling.
4. Model-based Policy analysis This demands for innovative computational modeling frameworks and tools. The latter require innovative computational model techniques: ➢Computational simulation ➢Metamodelling ➢Bayesian model averaging /model selection
5. Technical/Social Infrastructure CAADP-domains of Policy Interventions Food System Transformation Research & Extension Regional Development Non-agr sectoral development X1 X5 X4 X3 X2 Strategic Analysis Innovative Food System Modeling Framework CGPE – Akademiya2063-CAU Kiel Z1 Z5 Z4 Z3 Z2 Political Decision-Making G1 G3 G2 IG1 IG2 IG3 Legislative bargaining Stakeholder Participation Economic Growth Poverty Reduction Healthy Nutrition Sustainability Social Justice Policy Impact Analysis - Impact of multiple policies on multiple policy goals - Trade-off‘s between policy goals - Interaction between different policy interventions Constitutional Design - Coordinated vs uncoordinated ministerial policy choices - Centralized vs decentralized policy choices - Policy Choices under high vs low stakeholder participation Policy Learning - Policy Tools enabeling interactive knowledge exchange/communication between scientific model and practical politicians
6. Type II Model Output ZI=FI m(αI,XI m) Input Type I Model PMP-GLOBIOM National -Regional CGPE-Metamodeling Framework Community level Output ZII=FII m(αII,XII m) Input Type III Model Output ZIII=FIII m(αIII,XIII m) Input National CGE Socio-economic modules Poverty Nutrition
7. Module I Core - Model Innovative technology module 𝛽1 CGPE-Modeling Framework Module VI National CGE F(z, 𝛽)=0 Food Waste module 𝛽6 Module IV Module III Intra-African Trade module 𝛽3 PIFs 𝛽 = H(x,I(x)) Module II Land use change 𝛽2 Module V GHG-emission 𝛽5 Socio-economic modules Poverty 𝛽4 Nutrition
8. Empirical Data Scientific Model Expert Knowledge AI estimation Policy-Goal Relation Bayesian estimation Metamodeling CGPE- Model Data generating process Policy Instruments 𝑥 = (𝑥1 , … , 𝑥𝑖 , … , 𝑥𝑛 ) Policy Indicators 𝐼 = (𝐼1 , … , 𝐼𝑘 , … , 𝐼𝑚 ) Goal Indicators Z = (𝑍1 , … , 𝑍𝑘 , … , 𝑍𝑚 ) Simulation data BR-data Learning Learning Governmental Procedures Policy Strategies Policy Beliefs
9. AGRODEP AGRODEP Networks • Impact Evaluation • AI & Remote Sensing • Modeling & Tools • RIMA • Climate modeling • Governance Data & Model Library Training Centre School • Farm survey data • AAGWA Watch • SAM library • Trade data • ReSAKSS data • BR-data • AATM data • Afrobarometer • Stakeholder database Digital Knowledge and Learning System Task Forces • Econometrics • Machine Learning • Computational Modeling • Resilience assessment • Policy Analysis • Political Economy modeling • AI Remote sensing technology • Digital twin technology • Global Climate - Food Clubs • African Computational Political Economy model PAAS • NAIPs • RAIPS • African Energy policy initiatives • Global climate policy • Global Policy initiatives