Bot Contests - Learnings from Trading Agent Contest for SCM Maria Gini Computer Science and Engineering University of Minnesota Work with John Collins, Will Groves, and Wolf Ketter

Trading Agent Competition for Supply Chain Management (TAC SCM) Competition started in 2003 Server hosted at U Minnesota from 2008 to 2012.

Specifications of TAC SCM Six autonomous agents: Compete to acquire parts from suppliers. Assemble parts into computers. Compete for customer orders. The simulation takes place over 220 virtual days, each lasting 15 seconds of real time. At the end (game/tournament), the agent with the most money in its account is the winner. Supply and demand varies randomly through the game and across three market segments (low, medium, and high computer price).

Sequence of events Each request to a supplier for a specific component includes a quantity, lead time (the number of days before the order is delivered), and reserve price (the maximum the agent is willing to pay for the parts). Suppliers respond with offers that specify actual quantities, prices, and lead times. When an agent places an order, parts are scheduled for delivery into their stock. Agents can respond to requests made by customers for finished PCs. These requests specify quantity, due date, reserve price, and PC type. Agents compete for each customer request by submitting offers with a specific price. The agent with the lowest price for each request gets an order and upon delivery the revenue for the transaction is placed in its bank account.

Problems with the initial design Agents could easily manipulate the game The balance between supply and demand was simple, so an agent that kept its factory busy was successful

Redesign Agents have to keep their reputation, otherwise they might not get the supplies they need Suppliers keep some capacity for future demand, so it is very difficult to corner the market for some component types Agents see only the highest and lowest order prices in the sales market Expected customer demand is lower than production capacity, so keeping the factory busy will result in unsold inventory. But in same games supplies are lower than demand, so agents need to adapt The market is an oligopoly, so agents affect each other. Specific actions of other agents are not visible, only aggregate effects on the market are visible

Log file

Supply and demand Supply and demand in the procurement market for component #200 for lead times of 0 to 10 days from supplier Basus in tournament round tac01-#3454 Supply and demand for product #8 in experiment tac01-#3454. The demand line corresponds to the total market demand for that day, sorted by decreasing reserve price. The dotted lines correspond to each agent's offers (y-axis indicates offer price). The agent with the lowest offer price for a given request will receive the order. The right graph has only the 3 agents that made offers.

Price Volatility Daily volatility for the mean price of components with lead time ≤6 for simulation run tac02-#3183. The volatility is measured as standard deviation of observed unit prices within a 10-day window for each component line Customer market normalized mean daily sales price volatility (1 day volatility) by market segment (low, med, high) in experiment tac02-#3183.

Component inventory, supply velocity, and sales velocity for agent TacTex for component 110 In tac01-#3454. As product demand increases (becomes more negative), the agent reduces sales velocity and increases procurement velocity. When the demand exceeds some threshold, the agent increases sales velocity as the per-unit price peaks.

Bidding price trends for top 3 agents for product 8 in experiment tac02-#2069. Each point along an agent's series denotes an instance when the agent placed a winning bid (all winning bids for the selected agents are shown, bids of other agents are omitted for clarity). The y-axis value denotes the difference between the agent's bid price and the previous day mean winning bid price. Since daily price changes are small, the previous day's mean price is a good estimate of the current day's price. Wolfgang Ketter, John Collins, Maria Gini, Alok Gupta, and Paul Schrater. Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges. Decision Support Systems, 47(4):307–318, 2009

Making predictions Economic theory suggests that markets exhibit 3 dominant statistical conditions: over-supply, balanced, and scarcity. We call these distinguishable conditions regimes. Knowledge of current and anticipated regimes can enable an agent to make better operational and strategic decisions. Wolfgang Ketter, John Collins, Maria Gini, Alok Gupta, and Paul Schrater. Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes. Information Systems Research, pp. 1263–1283, December 2012

Steps Offline identification of regimes from past game data (using a Gaussian mixture model and the EM algorithm per market segment) Online identification of current regimes using data available in current game (estimate regime by computing normalized prize from daily price report and selecting regime with highest probability) Prediction of regime transitions (using a Markov process for regime prediction and a semi-Markov process for time of transition)

What’s next? Time to move from academia to real world? What would it take to do it? What is missing? Thanks for your attention! For info contact gini@umn.edu http://www.cs.umn.edu/~gini/publications