[184] | 1 | package bilateralexamples;
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[151] | 2 |
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| 3 | import java.util.List;
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| 4 |
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| 5 | import genius.core.Bid;
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| 6 | import genius.core.Domain;
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[182] | 7 | import genius.core.actions.Accept;
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[151] | 8 | import genius.core.actions.Action;
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| 9 | import genius.core.actions.Offer;
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| 10 | import genius.core.parties.AbstractNegotiationParty;
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| 11 | import genius.core.parties.NegotiationInfo;
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[182] | 12 | import genius.core.timeline.DiscreteTimeline;
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| 13 | import genius.core.timeline.Timeline.Type;
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[151] | 14 | import genius.core.uncertainty.BidRanking;
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| 15 | import genius.core.uncertainty.ExperimentalUserModel;
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| 16 | import genius.core.utility.AbstractUtilitySpace;
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| 17 | import genius.core.utility.CustomUtilitySpace;
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| 18 |
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[166] | 19 | /**
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[167] | 20 | * Example of a party that deals with preference uncertainty by defining a custom UtilitySpace
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| 21 | * based on the closest known bid.
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| 22 | *
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| 23 | * Given a bid b and a preference ranking o1 <= o2 <= ... < on from the user model, it does the following:
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| 24 | * It finds the outcome oi that is 'most similar' to b (in terms of overlapping values)
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| 25 | * It then estimates u(b) to be: (i / n) * (highestUtil - lowestUtil)
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| 26 | *
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| 27 | * Note that this agent's estimate of the utility function is not linear additive.
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[187] | 28 | *
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[166] | 29 | */
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[182] | 30 | @SuppressWarnings("serial")
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[168] | 31 | public class CustomUtilitySpacePartyExample extends AbstractNegotiationParty
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[151] | 32 | {
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[187] | 33 | /** This agent will make bids above the minimuTarget */
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| 34 | private double minimumTarget = 1;
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[151] | 35 |
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| 36 | @Override
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| 37 | public void init(NegotiationInfo info)
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| 38 | {
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| 39 | super.init(info);
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[182] | 40 | log("This is an example of a party that deals with preference uncertainty by defining a Custom UtilitySpace estimate.");
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[151] | 41 | log("The user model is: " + userModel);
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[182] | 42 | if (!hasPreferenceUncertainty())
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[167] | 43 | {
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| 44 | log("There is no preference uncertainty. Try this agent with a negotiation scenario that has preference uncertainty enabled.");
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| 45 | return;
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| 46 | }
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| 47 |
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| 48 | log("Lowest util: " + userModel.getBidRanking().getLowUtility()
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| 49 | + ". Highest util: " + userModel.getBidRanking().getHighUtility());
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| 50 | log("The estimated utility space is: " + getUtilitySpace());
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[151] | 51 |
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| 52 | Bid randomBid = getUtilitySpace().getDomain().getRandomBid(rand);
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[167] | 53 | log("The estimate of the utility of a random bid (" + randomBid + ") is: " + getUtility(randomBid));
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[151] | 54 |
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[167] | 55 | if (userModel instanceof ExperimentalUserModel)
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| 56 | {
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[151] | 57 | log("You have given the agent access to the real utility space for debugging purposes.");
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| 58 | ExperimentalUserModel e = (ExperimentalUserModel) userModel;
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| 59 | AbstractUtilitySpace realUSpace = e.getRealUtilitySpace();
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| 60 |
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| 61 | log("The real utility space is: " + realUSpace);
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| 62 | log("The real utility of the random bid is: "
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| 63 | + realUSpace.getUtility(randomBid));
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| 64 | }
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| 65 | }
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| 66 |
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[167] | 67 | /**
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[182] | 68 | * A simple concession function over time, accepting in the last rounds
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[167] | 69 | */
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[151] | 70 | @Override
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| 71 | public Action chooseAction(List<Class<? extends Action>> possibleActions)
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| 72 | {
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[187] | 73 | if (timeline.getType() != Type.Rounds || !hasPreferenceUncertainty())
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[182] | 74 | {
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[187] | 75 | log("This agent displays more interesting behavior with a round-based timeline and preference uncertainty; now it simply generates random bids.");
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[182] | 76 | return new Offer(getPartyId(), generateRandomBid());
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| 77 | }
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[187] | 78 |
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[182] | 79 |
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[187] | 80 | // Sample code that accepts offers in the last 3 rounds,
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| 81 | // or offers that appear in the top 10% of offers in the user model
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| 82 | if (getLastReceivedAction() instanceof Offer)
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[182] | 83 | {
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[184] | 84 | Bid receivedBid = ((Offer) getLastReceivedAction()).getBid();
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[187] | 85 |
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| 86 | // Accept in the last 3 rounds if the received bid is better than the reservation value
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| 87 | DiscreteTimeline t = (DiscreteTimeline) timeline;
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| 88 | if (t.getOwnRoundsLeft() < 3 && getUtility(receivedBid) >= utilitySpace.getReservationValue())
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| 89 | return new Accept(getPartyId(), receivedBid);
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| 90 |
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[184] | 91 | List<Bid> bidOrder = userModel.getBidRanking().getBidOrder();
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| 92 |
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| 93 | // If the rank of the received bid is known
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| 94 | if (bidOrder.contains(receivedBid)) {
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| 95 | double percentile = (bidOrder.size()
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| 96 | - bidOrder.indexOf(receivedBid))
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| 97 | / (double) bidOrder.size();
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| 98 | if (percentile < 0.1)
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| 99 | return new Accept(getPartyId(), receivedBid);
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| 100 | }
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| 101 | }
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[182] | 102 |
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[187] | 103 | // Return a random, conceding offer above minimumTarget
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[151] | 104 | Bid randomBid;
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| 105 | do
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| 106 | {
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| 107 | randomBid = generateRandomBid();
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[187] | 108 | minimumTarget *= 0.999;
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[151] | 109 | }
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[187] | 110 | while (getUtility(randomBid) < minimumTarget);
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[151] | 111 | return new Offer(getPartyId(), randomBid);
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| 112 | }
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| 113 |
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| 114 | /**
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| 115 | * We override the default estimate of the utility
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| 116 | * space by using {@link ClosestKnownBid} defined below.
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| 117 | */
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| 118 | @Override
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| 119 | public AbstractUtilitySpace estimateUtilitySpace()
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| 120 | {
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| 121 | return new ClosestKnownBid(getDomain());
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| 122 | }
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| 123 |
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| 124 | @Override
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| 125 | public String getDescription() {
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| 126 | return "Example agent with a custom utility space";
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| 127 | }
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| 128 |
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[182] | 129 | /**
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| 130 | * Defines a custom UtilitySpace based on the closest known bid to deal with preference uncertainty.
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| 131 | */
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[151] | 132 | private class ClosestKnownBid extends CustomUtilitySpace
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| 133 | {
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| 134 |
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| 135 | public ClosestKnownBid(Domain dom) {
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| 136 | super(dom);
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| 137 | }
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| 138 |
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| 139 | @Override
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| 140 | public double getUtility(Bid bid)
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| 141 | {
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| 142 | Bid closestRankedBid = getClosestBidRanked(bid);
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| 143 | return estimateUtilityOfRankedBid(closestRankedBid);
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| 144 | }
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| 145 |
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| 146 | public double estimateUtilityOfRankedBid(Bid b)
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| 147 | {
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| 148 | BidRanking bidRanking = getUserModel().getBidRanking();
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[182] | 149 | Double min = bidRanking.getLowUtility();
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| 150 | double max = bidRanking.getHighUtility();
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| 151 |
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[151] | 152 | int i = bidRanking.indexOf(b);
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| 153 |
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[182] | 154 | // index:0 has utility min, index n-1 has utility max
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| 155 | return min + i * (max - min) / (double) bidRanking.getSize();
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[151] | 156 | }
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| 157 |
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[167] | 158 | /**
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| 159 | * Finds the bid in the bid ranking that is most similar to bid given in the argument bid
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| 160 | */
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[151] | 161 | public Bid getClosestBidRanked(Bid bid)
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| 162 | {
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| 163 | List<Bid> bidOrder = getUserModel().getBidRanking().getBidOrder();
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| 164 | Bid closestBid = null;
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| 165 | double closestDistance = Double.MAX_VALUE;
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| 166 |
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| 167 | for (Bid b : bidOrder)
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| 168 | {
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| 169 | double d = 1 / (double) b.countEqualValues(bid);
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| 170 | if (d < closestDistance)
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| 171 | {
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| 172 | closestDistance = d;
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| 173 | closestBid = b;
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| 174 | }
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| 175 | }
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| 176 | return closestBid;
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| 177 | }
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| 178 |
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| 179 | }
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[167] | 180 |
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| 181 | private static void log(String s)
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| 182 | {
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| 183 | System.out.println(s);
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| 184 | }
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[151] | 185 |
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| 186 | }
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