Indian Food Supply Chains: A Game and Model to study Economic Behavior

Sebastiaan Meijer, Jayanth Raghothama, Robin King, Bharath M Palavalli

Next Generation Infrastructure Lab,Center for Study of Science, Technology and

Policy

Agenda

• Model of semi-perishable food chains, based on real data

• Initial validations and applications• Discussion of applicability of such methods in

the context of developing countries

Indian Agriculture & Horticulture

• 60% of India’s population subsist on agriculture• Less than 15% of India’s income• 20% price inflation on Agricultural products during the past year• Inflation despite buffer stocks and exports and growing

production• India accounts for 11% of world production in vegetables, and

15% of fruits• Total production volumes of around 230 Million Tonnes• India accounts for only 2% of global exports• Only 2% produce processed• Nearly 30% wasted ~ 60 Million tonnes

Data Sources: World Bank; Ministry of Agriculture, Government of India

Mango: An Example

• India accounts for 40% of world production, 25% of exports

• Over 300 varieties of Mangos grown; differentiated by price, use, location and quality (and taste)

• Prices range from Rs 8/kg to Rs 300

Mango: An Example

• Investigate the questions of waste, power structures and inefficiencies in the supply chain using Mango as an example

• Surveys in “mandis” (markets) around Bangalore, in Karnataka, interviews with farmers and other stakeholders

• Mapping of supply chain, and data collection to feed into an agent based model, and later a game

• Model based on surveys and supply/demand data sourced from Govt. of India

Problems within the supply chain

• APMC (Agricultural Produce Marketing Committee) Act: Mandates a government licensed intermediary through whom products have to be sold

• Created a layer of Middle-men • Middle men provide services in a bundle to

farmers ahead of harvest, creating lock-in and skewed power structures

• High logistical and intermediary costs, credit constraints, inadequate infrastructures, incomplete information etc.

Mango Mandi Gaming Simulation

Agent Model can run independent of the game. Players in the game compete and negotiate with software agents.

Attributes• Agents located in five cities,

four cities who produce and one who only consumes mangos

• Game lasts for 45 days, in compressed time

• Inventory every day to all agents based on supply/demand data

• Mangos: Ripeness, type, price

• All attributes updated every day

Agent PreferencesAgent Type Mango

Attributes (ordered, weighted)

Scrooge Factor (greed, 0 to 1)

History (preference for previous agents, 0 to 1)

Transport Type

Dealer 1. Ripeness2. Type3. Price

Very High Very High Equal preference for all types

Exporter 1. Type2. Price

Moderate High None

Small Vendor 1. Type2. Price3. Ripeness

Very Low Low None

Super Market 1. Type2. Ripeness3. Price

Moderate to High

Low None

Farmer Very Low Moderate to High

Cheapest option preferred.

Validation

• Game is a work in progress• Structure of the underlying ABM based on

primary data collected in “mandis” around Bangalore

• Processes used by agents derived from interviews and various government regulations

• Varied supply chain structures mean it can be validated only within a particular context

Applications• Institutional economics experiments: to

determine factors that drive actions in the supply chain

• Agent-human interaction: study of how effectively agents can interact with human players and vice-versa

• Understand actor behavior to better design interventions in the supply chain

Applicability• Severe lack of data• Data when available is not disaggregated, or

consistent• Context too varied to arrive at a generalized

problem definition and structure• Can you “translate” a model from one context

to another?

Acknowledgements: Jamsetji Tata Trust, India and Next Generation Infrastructures

Foundation, The Netherlands

Thank you