This post contains my design for our LFEG simulation based on a FACET or PlatBox framework. This entry will be constantly updated as my ideas and information change.
Agent: BuyerX
(Note: There will be many different buyer agents in the simulation: for each different combination of the three variables (preference for locally produced food, importance of price, and income level), there will be a different buyer agent. For example, someone whose preference for locally produced food (PLPF) coeffcient is .5, importance of price (IOP) coeffecient is .3, and income level is $50,000 to $74,999 would be represented by a different buyer agent than someone who has a PLPF coeffecient of .5, IOP coeffecient of .3, and income level of $75,000 to $99,999.)
(Note: A buyer agent can represent either an individual or a household.)
Examples of buyer agents:
(Note: The numbers included are merely for the purposes of this example; they are not based upon real data.)
Agent: Buyer1
The agent Buyer1, which represents 75 people, greatly prefers locally produced foods, does not consider price to be a very important factor in buying decisions, and is financially well off.
Agent: Buyer2
The agent Buyer2, which represents 110 people, slightly prefers locally produced foods, but will make buying decisions based almost exclusively on price, and has a fairly low income.
Agent: Buyer3
Buyer3, which represents 75 people, is indifferent about locally produced foods, considers price as an important aspect of buying decisions, and has an average income.
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Agent: SellerY
*Price index will be computed by calculating the prices of a representative bundle of goods (say, a bag of ten apples, a bag of chips, a pound of green beans, a gallon of milk, and a basket of strawberries) at each store, and putting the price of this bundle on a scale of 0 to 1.0, with 0 being the cheapest bundle surveyed, and 1.0 being the most expensive.
(Note: Each entity that sells food--with the exception of restaurants--will be represented by a different seller agent.)
(Note: It would ideal to have some information about the revenue/profits of each seller, but, as the stores will not give them out themselves, I am still working on a way to obtain this information).
Examples of seller agents:
(Note: The numbers included are merely for the purposes of this example; they are not based upon real data.)
Agent: Walmart
Agent: McNally's
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