单赢者选举最常见的投票系统之一是复数投票法。简而言之，得票最多的候选人获胜。但是，从数学上讲，多元投票是不合理的，并且容易造成选民被驱使投票赞成“两个邪恶中的较小者”，而不是他们真正喜欢的候选人。

在这个游戏中，您将编写利用多个投票系统的程序。它将在选举中为三名候选人之一投票。每个候选人都为自己带来一定的回报，而您的目标是使您的预期回报最大化。

收益是“均匀地”随机分布的，随每次选举而变化，并增加到100。候选人A可以拥有40收益，候选人B可以拥有27收益，候选人C可以拥有33收益。每个玩家都有一组不同的收益。

轮到您投票时，您将获得不完整的信息。下面列出的是您将可以使用的信息。由于您不知道其他玩家的个人收益是多少，因此根据当前的投票结果预测他们如何投票将是您的挑战。

• 到目前为止的部分选举结果
• 尚未投票的参与者（不包括您自己）的数量
• 您为每位候选人的个人收益
• 每个候选人的团体总收益

在给每个玩家投票的机会之后，投票最多的候选人将按照多次投票获胜。然后，每个玩家都会收到与该候选人的收益相对应的积分数。如果票数相等，则分配的分数将是并列候选人的平均值。

比赛结构

首次实例化时，将告知参赛者在锦标赛中举行的选举次数。我将尝试进行大量的选举。然后，每次选举将一一进行。

参加者洗牌后，每个人都有投票权。他们只能获得上面列出的有限信息，并返回一个数字表示他们的投票。每次选举结束后，将为每个漫游器提供最终的民意调查结果，并从该选举中增加其得分。

在举行了许多次选举之后，得奖者将是总分最高的人。控制器还通过将每个参赛者的得分与为随机投票机器人预测的得分分布进行比较来计算每个参赛者的“标准化”得分。

提交内容

提交将采用Java 8类的形式。 每个参赛者必须实现以下接口：

public interface Player
{
    public String getName();
    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs);
    public void receiveResults(int[] voteCounts, double result);
}

• 您的构造函数应采用一个int作为参数，该参数将代表将举行的选举次数。
• 该getName()方法返回要在排行榜上使用的名称。这样您就可以拥有格式精美的名称，只是不要发疯。
• 该getVote(...)方法返回0，1或2以表示将获得投票的候选人。
• 该receiveResults(...)方法主要是为了实现使用历史数据的更复杂策略的存在。
• 您几乎可以创建任何其他想要记录和处理提供给您的信息的方法/实例变量。

比赛周期，扩大

1. 每个参赛者都以实例化new entrantName(int numElections)。
2. 每次选举：
   1. 控制器为这次选举随机确定每个玩家的收益。下面给出了此代码。然后，它洗牌并让他们开始投票。
   2. 参赛者的方法public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)被调用，并且参赛者返回他们的投票0，1或2为自己选择的候选人。
   3. 参赛者 getVote(...)方法未返回有效投票的将被分配一个随机投票。
   4. 在所有人投票之后，控制器通过复数方法确定选举结果。
   5. 通过调用其方法，将最终投票计数及其收益告知参赛者public void receiveResults(int[] voteCounts, double result)。
3. 举行所有选举后，获胜者将是得分最高的人。

收益的随机分布

在精确的分配将对游戏一个显著的效果。我选择了具有大标准偏差（大约23.9235）的分布，并且该分布能够产生非常高和非常低的收益。我检查了三个收益中的每一个都有相同的分布。

public int[] createPlayerPayoffs()
{
    int cut1;
    int cut2;
    do{
        cut1 = rnd.nextInt(101);
        cut2 = rnd.nextInt(101);  
    } while (cut1 + cut2 > 100);
    int rem = 100 - cut1 - cut2;
    int[] set = new int[]{cut1,cut2,rem};
    totalPayoffs[0] += set[0];
    totalPayoffs[1] += set[1];
    totalPayoffs[2] += set[2];
    return set;
}

更多规则

这是一些更通用的规则。

• 您的程序不得运行/修改/实例化控制器或其他进入者或其记忆的任何部分。
• 由于您的程序在整个锦标赛中都保持“实时”运行，因此请勿创建任何文件。
• 请勿与其他任何参与程序互动，提供帮助或定位。
• 您可以提交多个参赛者，只要它们之间存在合理差异，并且您遵守上述规则即可。
• 我还没有指定一个确切的时间限制，但我将不胜感激那些运行时间显著低于每通话一秒钟。我希望能够举行尽可能多的选举。

控制器

控制器可以在这里找到。 主程序是Tournament.java。还有两个简单的漫游器，分别为RandomBot和竞争PersonalFavoriteBot。我将在答案中发布这两个机器人。

排行榜

看起来ExpectantBot是现任领导人，其次是Monte Carlo，然后是StaBot。

Leaderboard - 20000000 elections:
   767007688.17 (  937.86) - ExpectantBot                            
   766602158.17 (  934.07) - Monte Carlo 47                          
   766230646.17 (  930.60) - StatBot                                
   766054547.17 (  928.95) - ExpectorBot                             
   764671254.17 (  916.02) - CircumspectBot                          
   763618945.67 (  906.19) - LockBot                                 
   763410502.67 (  904.24) - PersonalFavoriteBot343                  
   762929675.17 (  899.75) - BasicBot                                
   761986681.67 (  890.93) - StrategicBot50                          
   760322001.17 (  875.37) - Priam                                   
   760057860.67 (  872.90) - BestViableCandidate (2842200 from ratio, with 1422897 tie-breakers of 20000000 total runs)
   759631608.17 (  868.92) - Kelly's Favorite                        
   759336650.67 (  866.16) - Optimist                                
   758564904.67 (  858.95) - SometimesSecondBestBot                  
   754421221.17 (  820.22) - ABotDoNotForget                         
   753610971.17 (  812.65) - NoThirdPartyBot                         
   753019290.17 (  807.12) - NoClueBot                               
   736394317.17 (  651.73) - HateBot670                              
   711344874.67 (  417.60) - Follower                                
   705393669.17 (  361.97) - HipBot                                  
   691422086.17 (  231.38) - CommunismBot0                           
   691382708.17 (  231.01) - SmashAttemptByEquality (on 20000000 elections)
   691301072.67 (  230.25) - RandomBot870                            
   636705213.67 ( -280.04) - ExtremistBot                            
The tournament took 34573.365419071 seconds, or 576.2227569845166 minutes.

这是一些较旧的锦标赛，但是自运行以来，这些机器人均未更改其功能。

Leaderboard - 10000000 elections:
   383350646.83 (  661.14) - ExpectantBot                            
   383263734.33 (  659.99) - LearnBot                                
   383261776.83 (  659.97) - Monte Carlo 48                          
   382984800.83 (  656.31) - ExpectorBot                             
   382530758.33 (  650.31) - CircumspectBot                          
   381950600.33 (  642.64) - PersonalFavoriteBot663                  
   381742600.33 (  639.89) - LockBot                                 
   381336552.33 (  634.52) - BasicBot                                
   381078991.83 (  631.12) - StrategicBot232                         
   380048521.83 (  617.50) - Priam                                   
   380022892.33 (  617.16) - BestViableCandidate (1418072 from ratio, with 708882 tie-breakers of 10000000 total runs)
   379788384.83 (  614.06) - Kelly's Favorite                        
   379656387.33 (  612.31) - Optimist                                
   379090198.33 (  604.83) - SometimesSecondBestBot                  
   377210328.33 (  579.98) - ABotDoNotForget                         
   376821747.83 (  574.84) - NoThirdPartyBot                         
   376496872.33 (  570.55) - NoClueBot                               
   368154977.33 (  460.28) - HateBot155                              
   355550516.33 (  293.67) - Follower                                
   352727498.83 (  256.36) - HipBot                                  
   345702626.33 (  163.50) - RandomBot561                            
   345639854.33 (  162.67) - SmashAttemptByEquality (on 10000000 elections)
   345567936.33 (  161.72) - CommunismBot404                         
   318364543.33 ( -197.86) - ExtremistBot                            
The tournament took 15170.484259763 seconds, or 252.84140432938332 minutes.

我还进行了第二次10m锦标赛，确认了ExpectantBot的领先优势。

Leaderboard - 10000000 elections:
   383388921.83 (  661.65) - ExpectantBot                            
   383175701.83 (  658.83) - Monte Carlo 46                          
   383164037.33 (  658.68) - LearnBot                                
   383162018.33 (  658.65) - ExpectorBot                             
   382292706.83 (  647.16) - CircumspectBot                          
   381960530.83 (  642.77) - LockBot                                 
   381786899.33 (  640.47) - PersonalFavoriteBot644                  
   381278314.83 (  633.75) - BasicBot                                
   381030871.83 (  630.48) - StrategicBot372                         
   380220471.33 (  619.77) - BestViableCandidate (1419177 from ratio, with 711341 tie-breakers of 10000000 total runs)
   380089578.33 (  618.04) - Priam                                   
   379714345.33 (  613.08) - Kelly's Favorite                        
   379548799.83 (  610.89) - Optimist                                
   379289709.83 (  607.46) - SometimesSecondBestBot                  
   377082526.83 (  578.29) - ABotDoNotForget                         
   376886555.33 (  575.70) - NoThirdPartyBot                         
   376473476.33 (  570.24) - NoClueBot                               
   368124262.83 (  459.88) - HateBot469                              
   355642629.83 (  294.89) - Follower                                
   352691241.83 (  255.88) - HipBot                                  
   345806934.83 (  164.88) - CommunismBot152                         
   345717541.33 (  163.70) - SmashAttemptByEquality (on 10000000 elections)
   345687786.83 (  163.30) - RandomBot484                            
   318549040.83 ( -195.42) - ExtremistBot                            
The tournament took 17115.327209018 seconds, or 285.25545348363335 minutes.

NoThirdPartyBot

该机器人试图猜测哪个候选人将排在第三，并在两个前锋中对他最喜欢的候选人进行投票。

import java.util.Arrays;
import java.util.Collections;
import java.util.List;

public class NoThirdPartyBot implements Player {

    public NoThirdPartyBot(int e) {
    }


    @Override
    public String getName() {
        return "NoThirdPartyBot";
    }

    @Override
    public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs,
            int[] totalPayoffs) {
        List<Integer> order = order(totalPayoffs);

        if (payoffs[order.get(0)] > payoffs[order.get(1)]) {
            return order.get(0);
        } else {
            return order.get(1);
        }
    }

    static List<Integer> order(int[] array) {
        List<Integer> indexes = Arrays.asList(0, 1, 2);
        Collections.sort(indexes, (i1, i2) -> array[i2] - array[i1]);
        return indexes;
    }

    @Override
    public void receiveResults(int[] voteCounts, double result) {
    }
}

情节机器人

该机器人为他的最爱（在数学上并未被淘汰）投票。

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;


public class CircumspectBot implements Player {

    public CircumspectBot(int elections) {
    }

    @Override
    public String getName() {
        return "CircumspectBot";
    }

    @Override
    public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs,
            int[] totalPayoffs) {
        List<Integer> indexes = new ArrayList<>();
        int topVote = Arrays.stream(voteCounts).max().getAsInt();
        for (int index = 0; index < 3; index++) {
            if (voteCounts[index] + votersRemaining + 1 >= topVote) {
                indexes.add(index);
            }
        }
        Collections.sort(indexes, (i1, i2) -> payoffs[i2] - payoffs[i1]);

        return indexes.get(0);
    }

    @Override
    public void receiveResults(int[] voteCounts, double result) {

    }

}

ExpectantBot

假设所有选民随后将随机投票，该机器人将计算每个投票选项的期望值。

import java.util.Arrays;

public class ExpectantBot implements Player {

    public ExpectantBot(int elections) {
    }

    @Override
    public String getName() {
        return "ExpectantBot";
    }

    static double choose(int x, int y) {
        if (y < 0 || y > x) return 0;
        if (y > x/2) {
            // choose(n,k) == choose(n,n-k), 
            // so this could save a little effort
            y = x - y;
        }

        double denominator = 1.0, numerator = 1.0;
        for (int i = 1; i <= y; i++) {
            denominator *= i;
            numerator *= (x + 1 - i);
        }
        return numerator / denominator;
    }

    double expectedPayout(int[] voteCounts, int[] payoffs, int votersRemaining) {
        double total = 0.0;
        for (int firstPartyVoters = 0; firstPartyVoters <= votersRemaining; firstPartyVoters++) {
            for (int secondPartyVoters = 0; secondPartyVoters <= votersRemaining - firstPartyVoters; secondPartyVoters++) {
                int thirdPartyVoters = votersRemaining - firstPartyVoters - secondPartyVoters;

                int [] newVoteCounts = voteCounts.clone();
                newVoteCounts[0] += firstPartyVoters;
                newVoteCounts[1] += secondPartyVoters;
                newVoteCounts[2] += thirdPartyVoters;
                int highest = Arrays.stream(newVoteCounts).max().getAsInt();
                int payoff = 0;
                int winCount = 0;
                for (int index = 0; index < 3; index++) {
                    if (newVoteCounts[index] == highest) {
                        payoff += payoffs[index];
                        winCount++;
                    }
                }
                double v = (double)payoff / (double) winCount;
                double value = choose(votersRemaining, firstPartyVoters)*choose(votersRemaining - firstPartyVoters, secondPartyVoters)*v*Math.pow(1/3.0, votersRemaining);
                total += value;
            }
        }
        return total;
    }

    @Override
    public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs,
            int[] totalPayoffs) {

        int bestVote = 0;
        double bestScore = 0.0;
        for (int vote = 0; vote < 3; vote++) {      
            voteCounts[vote]++;
            double score = expectedPayout(voteCounts, payoffs, votersRemaining);
            if (score > bestScore) {
                bestVote = vote;
                bestScore = score;
            }
            voteCounts[vote]--;
        }
        return bestVote;

    }

    @Override
    public void receiveResults(int[] voteCounts, double result) {   
    }

}

嘻哈

HipBot不在乎支出。金钱只是镇静剂，分散了真正的艺术品。

HipBot希望为真实的人投票，而不仅仅是某些公司的骗子。他还想在他们的（可能是）惨败之后穿上他们的运动衫，所以每当获胜者做错事情时，他都会感觉很出色。

因此，HipBot会投票给票数最低的人，或者如果有平局，则是获得更高报酬的人。只吃有机食品不是免费的。

public class HipBot implements Player{

    public HipBot(int rounds){ /*Rounds are a social construct*/ }

    public String getName(){ return "HipBot"; }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs){

        int coolest = 0;
        int lowest = 100000000;
        int gains = 0;

        for( int count = 0; count < voteCounts.length; count++ ){

            if( voteCounts[count] < lowest || (voteCounts[count] == lowest && payoffs[count] > gains) ){

                lowest = voteCounts[count];
                coolest = count;
                gains = payoffs[count];

            }

        }

        return coolest;

    }

    public void receiveResults(int[] voteCounts, double result){ /*The past is dead*/ }

}

HipBot也未经测试，所以请让我知道是否正在发生任何事情。

编辑：在更具竞争性的决胜局中添加了一些简短的评论。

个人收藏夹

该机器人只是为个人收益最高的候选人投票，而忽略了其他一切。这项挑战的重点之一就是证明这不是最佳策略。

import java.lang.Math;
import java.util.Random;
/**
 * This bot picks the candidate with the highest personal payoff, ignoring everyone else's actions.
 * 
 * @author PhiNotPi 
 * @version 5/27/15
 */
public class PersonalFavoriteBot implements Player
{
    Random rnd;
    String name;
    /**
     * Constructor for objects of class PersonalFavoriteBot
     */
    public PersonalFavoriteBot(int e)
    {
       rnd = new Random(); 
       name = "PersonalFavoriteBot" + rnd.nextInt(1000);
    }

    public String getName()
    {
        return name;
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        //return rnd.nextInt(3);
        int maxloc = 0;
        for(int i = 1; i< 3; i++)
        {
            if(payoffs[i] > payoffs[maxloc])
            {
                maxloc = i;
            }
        }
        return maxloc;
    }

    public void receiveResults(int[] voteCounts, double result)
    {

    }
}

RandomBot

该机器人随机投票。不论进行了多少次选举（只要它相当高，例如超过100次），该参赛者的标准化得分都在-2和2之间波动。

import java.lang.Math;
import java.util.Random;
/**
 * This bot votes for a random candidate.
 * 
 * @author PhiNotPi 
 * @version 5/27/15
 */
public class RandomBot implements Player
{
    Random rnd;
    String name;
    /**
     * Constructor for objects of class RandomBot
     */
    public RandomBot(int e)
    {
       rnd = new Random(); 
       name = "RandomBot" + rnd.nextInt(1000);
    }

    public String getName()
    {
        return name;
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        return rnd.nextInt(3);
    }

    public void receiveResults(int[] voteCounts, double result)
    {

    }
}

追随者

追随者想加入进来。它认为实现这一目标的最佳方法是，以与其他所有人相同的方式进行表决，或者至少以到目前为止的多数表决。它将根据自己的喜好打破联系，以显示出一点独立性。但不要太多。

public class Follower implements Player
{
    public Follower(int e) { }

    public String getName()
    {
        return "Follower";
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        int mostPopular = 0;
        int mostVotes = voteCounts[0];
        for (int i = 1; i < voteCounts.length; i++) {
            int votes = voteCounts[i];
            if (votes > mostVotes || (votes == mostVotes && payoffs[i] > payoffs[mostPopular])) {
                mostPopular = i;
                mostVotes = votes;
            }
        }
        return mostPopular;

    }

    public void receiveResults(int[] voteCounts, double result) { }
}

注意：我尚未对此进行测试，因此请告知是否有任何错误。

蒙特卡洛

这模拟了大量随机选举。然后，它选择使自己的利润最大化的选择。

import java.util.ArrayList;
import java.util.List;

public class MonteCarlo implements Player{

    private static long runs = 0;
    private static long elections = 0;

    public MonteCarlo(int e) {
        elections = e;
    }

    @Override
    public String getName() {
        return "Monte Carlo (difficulty " + (runs / elections) + ")";
    }

    @Override
    public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs, int[] totalPayoffs) {
        elections++;
        double[] predictedPayoffs = new double[3];
        long startTime = System.nanoTime();
        while (System.nanoTime() - startTime <= 200_000){ //Let's give us 200 micro-seconds.
            runs++;
            int[] simulatedVoteCounts = voteCounts.clone();
            for (int j = 0; j < votersRemaining; j++){
                simulatedVoteCounts[((int) Math.floor(Math.random() * 3))]++;
            }
            for (int j = 0; j < 3; j++) {
                simulatedVoteCounts[j]++;
                List<Integer> winners = new ArrayList<>();
                winners.add(0);
                for (int k = 1; k < 3; k++) {
                    if (simulatedVoteCounts[k] > simulatedVoteCounts[winners.get(0)]) {
                        winners.clear();
                        winners.add(k);
                    } else if (simulatedVoteCounts[k] == simulatedVoteCounts[winners.get(0)]) {
                        winners.add(k);
                    }
                }
                for (int winner : winners) {
                    predictedPayoffs[j] += payoffs[winner] / winners.size();
                }
                simulatedVoteCounts[j]--;
            }
        }
        int best = 0;
        for (int i = 1; i < 3; i++){
            if (predictedPayoffs[i] > predictedPayoffs[best]){
                best = i;
            }
        }
        return best;
    }

    @Override
    public void receiveResults(int[] voteCounts, double result) {

    }
}

StatBot

StatBot基于ExpectantBot；但是，不是假设每个投票都具有相同的可能性，而是收集人们如何投票的统计数据，并使用该统计数据来估计概率。

import java.util.Arrays;


public class StatBot implements Player {

    static private int[][][] data = new int[3][3][3];
    private int[] voteCounts;

    StatBot(int unused) {

    }

    @Override
    public String getName() {
        return "StatBot";

    }

     static double choose(int x, int y) {
            if (y < 0 || y > x) return 0;
            if (y > x/2) {
                // choose(n,k) == choose(n,n-k), 
                // so this could save a little effort
                y = x - y;
            }

            double denominator = 1.0, numerator = 1.0;
            for (int i = 1; i <= y; i++) {
                denominator *= i;
                numerator *= (x + 1 - i);
            }
            return numerator / denominator;
        }

    double expectedPayout(int[] voteCounts, int[] payoffs, int votersRemaining) {
        Integer[] indexes = {0, 1, 2};
        Arrays.sort(indexes, (i0, i1) -> voteCounts[i1] - voteCounts[i0]);
        int [] stats = data[indexes[0]][indexes[1]];
        int total_stats = Arrays.stream(stats).sum();
        double total = 0.0;
        for (int firstPartyVoters = 0; firstPartyVoters <= votersRemaining; firstPartyVoters++) {
            for (int secondPartyVoters = 0; secondPartyVoters <= votersRemaining - firstPartyVoters; secondPartyVoters++) {
                int thirdPartyVoters = votersRemaining - firstPartyVoters - secondPartyVoters;

                int [] newVoteCounts = voteCounts.clone();
                newVoteCounts[0] += firstPartyVoters;
                newVoteCounts[1] += secondPartyVoters;
                newVoteCounts[2] += thirdPartyVoters;
                int highest = 0;
                for (int h : newVoteCounts) {
                    if (h > highest) highest = h;
                }
                int payoff = 0;
                int winCount = 0;
                for (int index = 0; index < 3; index++) {
                    if (newVoteCounts[index] == highest) {
                        payoff += payoffs[index];
                        winCount++;
                    }
                }
                double v = (double)payoff / (double) winCount;
                double value = choose(votersRemaining, firstPartyVoters)*choose(votersRemaining - firstPartyVoters, secondPartyVoters)*v;
                value *= Math.pow((double)stats[0]/(double)total_stats, firstPartyVoters);
                value *= Math.pow((double)stats[1]/(double)total_stats, secondPartyVoters);
                value *= Math.pow((double)stats[2]/(double)total_stats, thirdPartyVoters);

                total += value;
            }
        }
        return total;
    }

    @Override
    public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs,
            int[] totalPayoffs) {

        int bestVote = 0;
        double bestScore = 0.0;
        for (int vote = 0; vote < 3; vote++) {      
            voteCounts[vote]++;
            double score = expectedPayout(voteCounts, payoffs, votersRemaining);
            if (score > bestScore) {
                bestVote = vote;
                bestScore = score;
            }
            voteCounts[vote]--;
        }
        voteCounts[bestVote]++;
        this.voteCounts = voteCounts;

        return bestVote;

    }

    @Override
    public void receiveResults(int[] endVoteCounts, double result) {
        Integer[] indexes = {0, 1, 2};
        Arrays.sort(indexes, (i0, i1) -> voteCounts[i1] - voteCounts[i0]);
        for(int i = 0; i < 3; i++){
            data[indexes[0]][indexes[1]][i] += endVoteCounts[i] - voteCounts[i];
        }
    }
}

最佳可行候选人

我的原始稿件经过了相当大的修改。这仍然会淘汰在给定剩余选票的情况下无法赢得任何候选人的情况，然后使用一种尝试优化相对收益的策略而非绝对。第一个测试是采用我的个人收益与每个候选人的总收益之比，在那儿寻找最佳价值。然后，我会寻找其他与最佳比率非常接近的比率，如果总收益低于最佳比率，我会选择该比率。希望这会降低其他玩家的收益，同时保持我的合理水平。

在我自己的测试中，该bot的性能几乎与原始bot相同，但还不完全相同。我们将不得不看看它在整个领域中的表现。

 /**
  * This bot picks the candidate with the highest relative payoff out of those
  * candidates who are not already mathematically eliminated.
  *
  * @author Ralph Marshall
  * @version 5/28/2015
  */

import java.util.List;
import java.util.ArrayList;


public class BestViableCandidate implements Player
{
    private static int NUM_CANDIDATES = 3;
    private int relativeCount = 0;
    private int relativeCountLowerTotal = 0;
    private int totalRuns;

    public BestViableCandidate(int r) {
        totalRuns = r;
    }

    public String getName() {
        return "BestViableCandidate (" + relativeCount + " from ratio, with " + relativeCountLowerTotal + " tie-breakers of " + totalRuns + " total runs)";
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs) {

        int i, maxVoteSoFar = 0;

        // First we figure out the maximum possible number of votes each candidate would get
        // if every remaining bot voted for it
        int [] maxPossibleVotes = new int[NUM_CANDIDATES];
        for (i = 0; i < NUM_CANDIDATES; i++) {

            // The voters remaining does not include me, so we need to add one to it
            maxPossibleVotes[i] = voteCounts[i] + votersRemaining + 1;

            if (voteCounts[i] > maxVoteSoFar) {
                maxVoteSoFar = voteCounts[i];
            }
        }

        // Then we throw out anybody who cannot win even if they did get all remaining votes
        List<Integer> viableCandidates = new ArrayList<Integer>();
        for (i = 0; i < NUM_CANDIDATES; i++) {
            if (maxPossibleVotes[i] >= maxVoteSoFar) {
                viableCandidates.add(Integer.valueOf(i));
            }
        }

        // And of the remaining candidates we pick the one that has the personal highest payoff
        // relative to the payoff to the rest of the voters
        int maxCandidate = -1;
        double maxRelativePayoff = -1;
        int maxPayoff = -1;
        int minTotalPayoff = Integer.MAX_VALUE;

        int originalMaxCandidate = -1;
        double originalMaxPayoff = -1;

        double DELTA = 0.01;

        double tiebreakerCandidate = -1;

        for (Integer candidateIndex : viableCandidates) {
            double relativePayoff = (double) payoffs[candidateIndex] / (double) totalPayoffs[candidateIndex];
            if (maxRelativePayoff < 0 || relativePayoff - DELTA > maxRelativePayoff) {
                maxRelativePayoff = relativePayoff;
                maxCandidate = candidateIndex;

                maxPayoff = payoffs[candidateIndex];
                minTotalPayoff = totalPayoffs[candidateIndex];

            } else if (Math.abs(relativePayoff - maxRelativePayoff) < DELTA) {
                if (totalPayoffs[candidateIndex] < minTotalPayoff) {
                    tiebreakerCandidate = candidateIndex;

                    maxRelativePayoff = relativePayoff;
                    maxCandidate = candidateIndex;

                    maxPayoff = payoffs[candidateIndex];
                    minTotalPayoff = totalPayoffs[candidateIndex];

                }
            }

            if (payoffs[candidateIndex] > originalMaxPayoff) {
                originalMaxPayoff = payoffs[candidateIndex];
                originalMaxCandidate = candidateIndex;
            }
        }

        if (tiebreakerCandidate == maxCandidate) {
            relativeCountLowerTotal++;
        }

        if (originalMaxCandidate != maxCandidate) {
            /*                System.out.printf("%nSelecting candidate %d with relative payoff %f (%d/%d) instead of %d with relative payoff %f (%d/%d)%n",
                              maxCandidate, (double) payoffs[maxCandidate]/(double)totalPayoffs[maxCandidate], payoffs[maxCandidate], totalPayoffs[maxCandidate],
                              originalMaxCandidate, (double) payoffs[originalMaxCandidate]/(double)totalPayoffs[originalMaxCandidate], payoffs[originalMaxCandidate], totalPayoffs[originalMaxCandidate]);
            */
            relativeCount++;
        }

        return maxCandidate;
    }
}

乐天派

乐观主义者非常乐观，并假设剩下的选民中有一半将投票给能够带来最佳回报的候选人。

import java.lang.Integer;
import java.lang.String;
import java.util.Arrays;
import java.util.Comparator;

public class Optimist implements Player
{
    public Optimist(int _) { }
    public String getName() { return "Optimist"; }
    public int getVote(int[] curVotes, int rem, final int[] payoffs, int[] _)
    {
        Integer[] opt = new Integer[] { 0, 1, 2 };
        Arrays.sort(opt, new Comparator<Integer>() { public int compare(Integer i1, Integer i2) { return payoffs[i1] > payoffs[i2] ? -1 : payoffs[i1] == payoffs[i2] ? 0 : 1; } });
        int a = curVotes[opt[0]], b = curVotes[opt[1]], c = curVotes[opt[2]];
        double rest = (double)rem / 4;
        if (b <= a + rest && c <= a + rest)
            return opt[0];
        else if (c <= b)
            return opt[1];
        else
            return opt[0];
    }
    public void receiveResults(int[] _, double __) { }
}

ABotDoNotForget

他的目标很简单：使用总收益确定总体趋势，并计算较低/中等/较高的趋势所赢得的时间。然后，他将投票给最有可能获胜的候选人。

import java.util.ArrayList;

public class ABotDoNotForget implements Player
{
    private int nbElec;
    private int countElec=0;
    private int[] currPayoffs=new int[3];
    private int[] lmh=new int[3];
    private int[] wins=new int[3];

    public ABotDoNotForget(int nbElec)
    {
        this.nbElec=nbElec;
    }

    public String getName() {return "ABotDoNotForget";}

    public int getVote(int[] voteCounts, 
                        int votersRemaining, 
                        int[] payoffs,
                        int[] totalPayoffs) 
    {
        countElec++;
        System.arraycopy(totalPayoffs, 0, currPayoffs, 0, totalPayoffs.length);

        if(countElec<=nbElec/20&&countElec<=20)
        {
            int best=0;
            for(int i=1;i<payoffs.length;i++)
                if(payoffs[i]>=payoffs[best])
                    best=i;
            return best;
        }

        for(int i =1;i<totalPayoffs.length;i++)
        {
            if(totalPayoffs[i]<totalPayoffs[i-1])
            {
                int tmp= totalPayoffs[i];
                totalPayoffs[i]=totalPayoffs[i-1];
                totalPayoffs[i-1]=tmp;
                if(i==2&&totalPayoffs[i-1]<totalPayoffs[i-2]){
                    tmp= totalPayoffs[i-1];
                    totalPayoffs[i-1]=totalPayoffs[i-2];
                    totalPayoffs[i-2]=tmp;
                }
            }
        }
        lmhDist(currPayoffs,totalPayoffs);
        int best=0;
        for(int i=1;i<wins.length;i++)
            if(wins[i]>=wins[best]){
                best=i;
            }
        int ownH=0;
        for(int i=1;i<payoffs.length;i++)
            if(payoffs[i]>=payoffs[ownH])
                ownH=i;
        int ownM=0;
        for(int i=1;i<payoffs.length;i++)
            if(payoffs[i]>=payoffs[ownM]&&i!=ownH)
                ownM=i;

        int persBest=(voteCounts[ownH]-voteCounts[ownM]+(votersRemaining/3)>=0
                &&voteCounts[ownH]-voteCounts[best]<(votersRemaining/3))?ownH:ownM;

        return persBest;

    }

    public void receiveResults(int[] voteCounts, double result) 
    {
        int best=0,bestV=voteCounts[best];
        for(int i=1;i<voteCounts.length;i++)
            if(voteCounts[i]>=bestV){
                best=i;
                bestV=voteCounts[i];
            }
        wins[lmh[best]]++;

    }

    private void lmhDist(int[] a,int[] s)
    {
        ArrayList<Integer> al = new ArrayList<Integer>();
        al.add(a[0]);al.add(a[1]);al.add(a[2]);
        lmh[0]=al.indexOf(s[0]);
        lmh[1]=al.indexOf(s[1]);
        lmh[2]=al.indexOf(s[2]);

    }
}

编辑：

普里阿姆

Priam讨厌递归。他根据总收益估算每个剩余机器人的概率，然后计算使收益最大化的最佳方法。

public class Priam implements Player {
    private static double[] smallFactorials = {1,1,2,6,24,120,720,5040,40320,362880,3628800,39916800,479001600,6227020800.,87178291200.,1307674368000.,20922789888000.,355687428096000.,6402373705728000.,121645100408832000.,2432902008176640000.};
    @Override
    public String getName() {
        return "Priam";
    }

    @Override
    public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs,
            int[] totalPayoffs) {
        int totalPayoff = totalPayoffs[0] + totalPayoffs[1] + totalPayoffs[2];
        double p0 = ((double)totalPayoffs[0])/totalPayoff;
        double p1= ((double) totalPayoffs[1])/totalPayoff;
        double p2 = ((double)totalPayoffs[2])/totalPayoff;
        double[] expectedPayoffs = {0,0,0};
        for(int myChoice=0;myChoice<3;myChoice++)
        {
            for(int x0 = 0; x0 <= votersRemaining; x0++)
            {
                for(int x1 = 0; x1 <= (votersRemaining-x0); x1++)
                {
                    int x2 = votersRemaining - (x1 + x0);
                    double probability =
                            Math.pow(p0, x0)
                            * Math.pow(p1, x1)
                            * Math.pow(p2, x2)
                            * Choose(votersRemaining, x0)
                            * Choose(votersRemaining-x0, x1);
                    int votes0 = voteCounts[0];
                    int votes1 = voteCounts[1];
                    int votes2 = voteCounts[2];
                    if(myChoice == 0)
                    {
                        votes0++;
                    }
                    else if(myChoice==1)
                    {
                        votes1++;
                    }
                    else
                    {
                        votes2++;
                    }

                    votes0+=x0;
                    votes1+=x1;
                    votes2+=x2;
                    if(votes0>votes1 && votes0>votes2)
                    {
                        expectedPayoffs[myChoice]+=probability*payoffs[0];
                    }
                    else if(votes1>votes2)
                    {
                        expectedPayoffs[myChoice]+=probability*payoffs[1];
                    }
                    else
                    {
                        expectedPayoffs[myChoice]+=probability*payoffs[2];
                    }
                }
            }
        }
        if(expectedPayoffs[0]>expectedPayoffs[1] && expectedPayoffs[0]>expectedPayoffs[2])
        {
            return 0;
        }
        else if(expectedPayoffs[1]>expectedPayoffs[2])
        {
            return 1;
        }
        else
        {
            return 2;
        }
    }

    private long Choose(int source, int team) {
        return Factorial(source)/(Factorial(team)*Factorial(source-team));
    }

    private long Factorial(int n) {
        if(n<=20)
        {
            return (long)smallFactorials[n];
        }
        double d=(double)n;
        double part1 = Math.sqrt(2*Math.PI*d);
        double part2 = Math.pow(d/Math.E, d);
        return (long)Math.ceil(part1 * part2);
    }

    @Override
    public void receiveResults(int[] voteCounts, double result) {


    }
    public Priam(int i)
    {

    }
}

它比Odysseus快得多，因为没有递归（运行时间为O（n ^ 2）），并且可以在15秒钟内进行一百万次选举。

NoClueBot

NoClue实际上并不非常了解Java或数学，因此他不知道这种权重比率对他是否会有所帮助。但是他在努力。

import java.lang.Math;
import java.util.*;
/**
 * Created by Admin on 5/27/2015.
 */
public class NoClueBot implements Player {

    public NoClueBot(int e) { }

    public String getName() {
        return "NoClueBot";
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs) {
        double x = 0;
        int y = 0;
        for (int i=0; i<3; i++) {
            double t = (double) voteCounts[i] * ((double) payoffs[i]/(double) totalPayoffs[i]);
            if (x<t) {
                x = t;
                y = i; 
            }
        }
        return y;
    }

    public void receiveResults(int[] voteCounts, double result) { }
}

SomeClueBot

SomeClueBot已退役。实际使用逻辑！过去使用逻辑，结果证明效率低下，所以他开始注意总收益，而不是自己的收益。再次使用逻辑！但是他对所有这些追随者和乐观主义者，甚至是不在乎的人，都做得不好！:)

有时SecondBestBot

基本上是PersonalFavouriteBot，在理论上有所改进。

import java.lang.Math;
/**
 * Created by Admin on 5/27/2015.
 */
public class SometimesSecondBestBot implements Player {
    public SometimesSecondBestBot(int e) { }

    public String getName() {
        return "SometimesSecondBestBot";
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs) {
        int m = 0;
        int n = 0;
        for(int i = 1; i< 3; i++) {
            if(payoffs[i] > payoffs[m]) { n = m; m = i; }
        }
        return (voteCounts[n]>voteCounts[m]&&totalPayoffs[n]>totalPayoffs[m])||(voteCounts[m]+votersRemaining<voteCounts[n])||voteCounts[m]+votersRemaining<voteCounts[Math.min(3-n-m, 2)] ? n : m;
    }

    public void receiveResults(int[] voteCounts, double result) { }
}

极端主义者

始终为收益最低的候选人投票

public class ExtremistBot implements Player
{
    public ExtremistBot(int e){}

    public void receiveResults(int[] voteCounts, double result){}

    public String getName(){
        return "ExtremistBot";
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        int min = 0;
        for(int i = 1; i<payoffs.length; i++){
            if(payoffs[i] <payoffs[min]){
                min = i;
            }
        }
        return min;
    }
}

SmashAttemptByEquality

我们的目标是均衡所有候选人，然后SMASH！上一轮所有其他机器人。
这是一个破坏性的算法，试图破坏所有其他算法，以赢得胜利。

public class SmashAttemptByEquality implements Player {
    static private int elections;

    public SmashAttemptByEquality(int e) { 
        this.elections = e;
    }

    public String getName() {
        return "SmashAttemptByEquality (on " + String.valueOf(this.elections) + " elections)";
    }

    public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs, int[] totalPayoffs) {

        //if there are no votes or it is a tie
        if(voteCounts.length == 0 || (voteCounts[0] == voteCounts[1] && voteCounts[1] == voteCounts[2]))
        {
            //let the system handle the (distributed?) randomness
            return 3;
        }

        //we want to win, so, lets not mess when there are no voters left
        if( votersRemaining > 0 )
        {
            //lets bring some equality!
            if( voteCounts[0] >= voteCounts[1] )
            {
                if(voteCounts[0] > voteCounts[2])
                {
                    return 2;
                }
                else
                {
                    return 0;
                }
            }
            else if( voteCounts[1] >= voteCounts[2] )
            {
                if(voteCounts[1] > voteCounts[0])
                {
                    return 0;
                }
                else
                {
                    return 1;
                }
            }
            else
            {
                return 0;
            }
        }
        else
        {
            //just play for the winner!
            if( voteCounts[0] >= voteCounts[1] )
            {
                if(voteCounts[0] > voteCounts[2])
                {
                    return 0;
                }
                else
                {
                    return 2;
                }
            }
            else if( voteCounts[1] >= voteCounts[2] )
            {
                if(voteCounts[1] > voteCounts[0])
                {
                    return 1;
                }
                else
                {
                    return 0;
                }
            }
            else
            {
                return 0;
            }
        }
    }

    public void receiveResults(int[] voteCounts, double result) { }
}

注意，这是未经测试的！

基本机器人

基本机器人只是投票给没有被淘汰的候选人，并且从这些候选人中获得最大的最大回报。

public class BasicBot implements Player {
    public BasicBot(int e) { }
    public String getName()
    {
        return "BasicBot";
    }
    public static int getMax(int[] inputArray){ 
    int maxValue = inputArray[0]; 
    for(int i=1;i < inputArray.length;i++){ 
      if(inputArray[i] > maxValue){ 
         maxValue = inputArray[i]; 
      } 
    } 
    return maxValue; 
   }
    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        // Check for Eliminated Candidates
        int eliminated0 = 0;
        int eliminated1 = 0;
        int eliminated2 = 0;
        if( ((voteCounts[0] + votersRemaining) < voteCounts[1]) || ((voteCounts[0] + votersRemaining) < voteCounts[2]))
        {
            eliminated0 = 1;
        }
        if( ((voteCounts[1] + votersRemaining) < voteCounts[0]) || ((voteCounts[1] + votersRemaining) < voteCounts[2]))
        {
            eliminated1 = 1;
        }
        if( ((voteCounts[2] + votersRemaining) < voteCounts[0]) || ((voteCounts[2] + votersRemaining) < voteCounts[1]))
        {
            eliminated2 = 1;
        }
        // Choose the Candidates that is not elimated with the largest payoff
        if ((payoffs[0] == getMax(payoffs)) && eliminated0 == 0)
            return 0
        else if ((payoffs[1] == getMax(payoffs)) && eliminated1 == 0)
            return 1
        else
            return 2

    }

    public void receiveResults(int[] voteCounts, double result)
    {
    }

}

凯利的最爱

我从CircumspectBot开始，但所剩无几。对剩余票数的概率分布进行无聊的猜测，然后做出最大化自身对数效用的选择（Kelly Criterion）。不是最快的，而是在其他一些球场内。而且，它在该领域具有相当的竞争力（就像我开始从事此工作并下载其他bot时那样）。

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import java.util.HashMap;

public class KellysFavorite implements Player {
    private ArrayList<Double> cache = new ArrayList<Double>();

    public KellysFavorite(int elections) {
        cache.add(0.0);
        double v = 0.0;
        for(int i=1; i<1000; i++) {
            v += Math.log(i);
            cache.add(v);
        }
    }

    @Override
    public String getName() {
        return "Kelly's Favorite";
    }

    private double factln(int n) {
        return cache.get(n);
    }

    private  double binll(int x, int n, double p)
    {
        double ll = 0.0;
        ll += ((double)x)*Math.log(p);
        ll += ((double)(n - x))*Math.log(1.0 - p);
        ll += factln(n) - factln(x) - factln(n-x);
        return ll;
    }

    public  double logAdd(double logX, double logY) {
        // 1. make X the max
        if (logY > logX) {
            double temp = logX;
            logX = logY;
            logY = temp;
        }
        // 2. now X is bigger
        if (logX == Double.NEGATIVE_INFINITY) {
            return logX;
        }
        // 3. how far "down" (think decibels) is logY from logX?
        //    if it's really small (20 orders of magnitude smaller), then ignore
        double negDiff = logY - logX;
        if (negDiff < -20) {
            return logX;
        }
        // 4. otherwise use some nice algebra to stay in the log domain
        //    (except for negDiff)
        return logX + java.lang.Math.log(1.0 + java.lang.Math.exp(negDiff));
    }

    @Override
    public int getVote(int[] voteCounts,
                       int votersRemaining,
                       int[] payoffs,
                       int[] totalPayoffs) {
        int totalviable = 0;
        boolean[] viable = { false, false, false };
        int topVote = Arrays.stream(voteCounts).max().getAsInt();
        for (int index = 0; index < 3; index++) {
            if (voteCounts[index] + votersRemaining + 1 >= topVote) {
                viable[index] = true;
                totalviable += 1;
            }
        }

        // if only one candidate remains viable, vote for them
        if(totalviable == 1) {
            for(int index = 0; index < 3; index++)
                if(viable[index])
                    return index;
        } else {
            double votelikelihoods[] = { 0.0, 0.0, 0.0 };
            double totalweight = 0.0;
            for(int index=0; index<3; index++) {
                if(!viable[index])
                    votelikelihoods[index] -= 10.0;
                else if(voteCounts[index] < topVote)
                    votelikelihoods[index] -= 0.1;

                totalweight += Math.exp(votelikelihoods[index]);
            }

            double probs[] = new double[3];
            for(int index=0; index<3; index++) {
                probs[index] = Math.exp(votelikelihoods[index]) / totalweight;
            }

            double[] utilities = {0,0,0};
            for(int mychoice=0; mychoice<3; mychoice++) {
                boolean seen[] = { false, false, false };
                double likelihoods[] = { Double.NEGATIVE_INFINITY,
                                         Double.NEGATIVE_INFINITY,
                                         Double.NEGATIVE_INFINITY };
                int[] localVoteCounts = { voteCounts[0] + (mychoice==0?1:0),
                                          voteCounts[1] + (mychoice==1?1:0),
                                          voteCounts[2] + (mychoice==2?1:0) };
                for(int iVotes=0; iVotes<=votersRemaining; iVotes++)
                    for(int jVotes=0; jVotes<=(votersRemaining-iVotes); jVotes++) {
                        int kVotes = votersRemaining - iVotes - jVotes;

                        int a = localVoteCounts[0] + iVotes;
                        int b = localVoteCounts[1] + jVotes;
                        int c = localVoteCounts[2] + kVotes;
                        int wincount = Math.max(a, Math.max(b, c));
                        int winners = 0;
                        if(a>=wincount) { winners += 1; }
                        if(b>=wincount) { winners += 1; }
                        if(c>=wincount) { winners += 1; }

                        double likelihood =
                            binll(iVotes, votersRemaining, probs[0])
                            + binll(jVotes, votersRemaining-iVotes, probs[1] / (probs[1] + probs[2]));

                        likelihood += Math.log(1.0/winners);

                        if(a>=wincount) {
                            if(seen[0])
                                likelihoods[0] = logAdd(likelihoods[0],
                                                        likelihood);
                            else
                                likelihoods[0] = likelihood;
                            seen[0] = true;
                        }
                        if(b>=wincount) {
                            if(seen[1])
                                likelihoods[1] = logAdd(likelihoods[1],
                                                        likelihood);
                            else
                                likelihoods[1] = likelihood;
                            seen[1] = true;
                        }
                        if(c>=wincount) {
                            if(seen[2])
                                likelihoods[2] = logAdd(likelihoods[2],
                                                        likelihood);
                            else
                                likelihoods[2] = likelihood;
                            seen[2] = true;
                        }

                    }

                for(int index=0; index<3; index++)
                    utilities[mychoice] += Math.exp(likelihoods[index]) * Math.log((double)payoffs[index]);
            }

            double maxutility = Math.max(utilities[0], Math.max(utilities[1], utilities[2]));
            int choice = 0;
            for(int index=0; index<3; index++)
                if(utilities[index]>=maxutility)
                    choice = index;
            return choice;
        }

        throw new InternalError();
    }

    @Override
    public void receiveResults(int[] voteCounts, double result) {

    }

}

如果更简单的话，也可以在https://gist.github.com/jkominek/dae0b3158dcd253e09e5获得。

共产主义

CommunismBot认为我们都应该相处并挑选最适合每个人的候选人。

public class CommunismBot implements Player
{
    Random rnd;
    String name;
    public CommunismBot(int e) {
        rnd = new Random(); 
        name = "CommunismBot" + rnd.nextInt(1000);
    }

    public String getName()
    {
        return name;
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        int maxloc = 0;
        for(int i = 1; i< 3; i++)
        {
            if(totalPayoffs[i] > totalPayoffs[maxloc])
            {
                maxloc = i;
            }
        }
        return maxloc;
    }

    public void receiveResults(int[] voteCounts, double result) { }
}

仇恨机器人

Hatebot总是选择最佳候选人。除非他们是一个肮脏的聚会1.那些家伙真可怕。

import java.util.Random;


public class HateBot implements Player
{
    Random rnd;
    String name;
    public HateBot(int e) {
        rnd = new Random(); 
        name = "HateBot" + rnd.nextInt(1000); }

    public String getName()
    {
        return name;
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        if(payoffs[0]>payoffs[2])
            return 0;
        else
            return 2;
    }

    public void receiveResults(int[] voteCounts, double result) { }
}

战略机器人

根据剩余的选民人数，StrategicBot对最佳候选人的投票应在与下一个最佳候选人的标准差之内。

import java.util.Random;

public class StrategicBot implements Player
{
    Random rnd;
    String name;
    public StrategicBot(int e) {
        rnd = new Random(); 
        name = "StrategicBot" + rnd.nextInt(1000);

    }

    public String getName()
    {
        return name;
    }

    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        double margin = 9.0*votersRemaining/9;
        int maxloc = 0;
        boolean noLead=false;
        for(int i = 1; i< 3; i++)
        {
            for(int j = 1; j < 3; j++)
            {
                if(payoffs[j] + margin > payoffs[i])
                    noLead=true;
            }
            if(payoffs[i] > payoffs[maxloc] && noLead)
            {
                maxloc = i;
            }
            noLead=false;
        }
        return maxloc;
    }

    public void receiveResults(int[] voteCounts, double result) { }
}

ExpectorBot

尝试通过计算其他机器人的平均支出来预测所有其他机器人的投票方式。默认的投票会获得最好的回报，但是如果期望票数比最佳票数多，那么我将投票给第二好，对我来说，平均派息要好于平均派，而最差派彩则有机会赢得这件事。

import java.util.Arrays;

public class ExpectorBot implements Player
{
    class Votee
    {
        int index;
        int payoff;
        float avgPayoff;
        float expectedVotes;
    }

    public ExpectorBot( final int e )
    {

    }

    @Override
    public String getName()
    {
        return "ExpectorBot";
    }

    @Override
    public int getVote( final int[] voteCounts, final int votersRemaining, final int[] payoffs, final int[] totalPayoffs )
    {
        final int otherVoters = Arrays.stream( voteCounts ).sum() + votersRemaining;
        final Votee[] v = createVotees( voteCounts, otherVoters, votersRemaining, payoffs, totalPayoffs );

        final Votee best = v[ 0 ]; // Most Payoff
        final Votee second = v[ 1 ];
        final Votee worst = v[ 2 ];

        int voteFor = best.index;

        if( ( second.expectedVotes >= best.expectedVotes + 1 ) // Second has more votes than Best even after I vote
                && ( second.payoff >= second.avgPayoff ) // Second payoff better than average for the others
                && ( worst.expectedVotes >= best.expectedVotes + 0.5f ) ) // Worst has a chance to win
        {
            voteFor = second.index;
        }

        return voteFor;
    }

    private Votee[] createVotees( final int[] voteCounts, final int otherVoters, final int votersRemaining, final int[] payoffs, final int[] totalPayoffs )
    {
        final Votee[] v = new Votee[ 3 ];

        for( int i = 0; i < 3; ++i )
        {
            v[ i ] = new Votee();
            v[ i ].index = i;
            v[ i ].payoff = payoffs[ i ];

            // This is the average payoff for other Players from this Votee
            v[ i ].avgPayoff = (float)( totalPayoffs[ i ] - payoffs[ i ] ) / otherVoters;

            // The expected number of Votes he will get if everyone votes for biggest payoff
            v[ i ].expectedVotes = voteCounts[ i ] + ( votersRemaining * v[ i ].avgPayoff / 100.0f );
        }

        Arrays.sort( v, ( o1, o2 ) -> o2.payoff - o1.payoff );

        return v;
    }

    @Override
    public void receiveResults( final int[] voteCounts, final double result )
    {

    }
}

锁扣

只是一个孤独的哲学家，正在寻找他的“ e” ...

//He thinks he's the father of democracy, but something's missing....
public class LockBot implements Player {

public LockBot(int i) {
    //One election, 10000000, what's the difference?
}

@Override
public String getName() {
    return "LockBot";
}

@Override
public int getVote(int[] voteCounts, int votersRemaining, int[] payoffs,
        int[] totalPayoffs) {

    double totalPlayers = voteCounts.length + votersRemaining;
    double totalPayoff = totalPlayers * 100;

    //adjust total payoffs to ignore my own
    for( int i = 0; i < totalPayoffs.length; i++){
        totalPayoffs[i] -= payoffs[i];
    }

    //Votes are probably proportional to payoffs
    //So lets just find the highest weight
    double[] expectedOutcome = new double[3];
    for(int i = 0; i< expectedOutcome.length; i++){
        expectedOutcome[i] = (totalPayoffs[i] / totalPayoff) * payoffs[i];
    }

    //Find the highest
    int choice = 0;
    if(expectedOutcome[1] > expectedOutcome[choice]){
        choice = 1;
    }
    if(expectedOutcome[2] > expectedOutcome[choice]){
        choice = 2;
    }




    return choice;
}

@Override
public void receiveResults(int[] voteCounts, double result) {
    // TODO Auto-generated method stub

}

}

输赢

如果你赢了，我输了！这么简单。因此，该机器人投票给了他喜欢的人，其他所有人都不喜欢的人。

public class WinLose implements Player
{
    public WinLose(int e) { }

    public String getName()
    {
        return "WinLose";
    }
    public int getVote(int [] voteCounts, int votersRemaining, int [] payoffs, int[] totalPayoffs)
    {
        int max = 0;
        for(int i = 1; i< 3; i++)
        {
            if(10*payoffs[i]-totalPayoffs[i] > 10*payoffs[max]-totalPayoffs[max])
            {
                max = i;
            }
        }
        return max;
    }

    public void receiveResults(int[] voteCounts, double result)
    {

    }
}