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1 z-tree

2 XLS SeparateTable z-tree z-tree

3 Summary Parameters

4 1 z-tree ztree2 ztree2 ORC 4. z-tree 5. results : results results 6. z-tree : 1 1. z-tree zleaf2 zleaf2 ORC z-leaf z-leaf 4

5 Windows Linux 2. Windows ztree ztree 3. z-tree 4. results result 5. z-tree 1. z-tree zleaf zleaf ORC File Open Background General Parameters Number of subjects zleaf Number of groups Practice period Paying period 2. zleaf 3. Run Start Treatment 5

6 1.5 Run Client s Table z-tree z-leaf Run Subjects Table 1.6 Run Start Treatment 1.7 File Open z-tree BasicGames Question Run Start Questionnaire Questionnaire z-tree Questionnaire XXYYZZQQ pay z-tree ID 6

7 2 2.1 XLS result result Ctrl+Alt+Del z-tree result XXYYZZQQ xls XXYYZZ QQ z-tree 1. C 2. C subjects contract 2.2 SeparateTable SeparateTable 1. z-tree z-tree 2. xls Ctrl+Alt+Del z-tree 3. z-tree Tools Separate Tables 4. xls OK 5. grobal, session, subjects, summary 4 subjects D Period E Subject zleaf F Group 7

8 G Profit 2.3 xls SeparateTable XYYZZQQ subjects.xls 2 D Period E Subject, F Group, M Contribution Period 1 Subjects Group 3 2 Period Subject 1 1 Group Contribution 8

9 3 Period xls SeparateTable 5 E Subject, F Group, 9

M Contribution 2. Subject Group 5 5 (1) 3. Contribution 6 4.

10 M Contribution 2. Subject Group 5 5 (1) 3. Contribution a 2 C19 ( 7 ) b ( 8 ) c ( 9 ) d C19 e D2 f 3 D19) ( 10 ) g 3 10

11 6 (2) Contribution 7 (3) 11

12 8 (4) 9 (5) 5. :SPSS 12

2.5 10 Globals SELLERTYPE BUYERTYPE Grobal Subjects Subjects Contracts Subjects Subjects 1,2,3,4 4 1 4

13 Globals SELLERTYPE BUYERTYPE Grobal Subjects Subjects Contracts Subjects Subjects 1,2,3, Type 2,1,2,1 Globals 1 Type Type Contracts Creator, Price, Buyer, Seller 4 Buyer Seller Price Price Creator 13

2 Creator Price 200 Seller Buyer 2 200 1 2 1 200 11 3 Creator Price 180 Seller Buyer 180 4 3 4 4

14 2 Creator Price 200 Seller Buyer Creator Price 180 Seller Buyer Seller Buyer

15 15

16 3 z-tree 3.1 z-tree PC PC PC 1. PC 2. student student 3. b z-tree 4. z-tree z-tree 5. a Untitled Treatment 1 [ ] b Treatment Language Nihongo c File New Treatment d OK 6. z-leaf CD-ROM 2 z-leaf z-leaf1 z-leaf2 2 2 z-leaf 3 zleaf3 z-leaf 1 z-leaf2 zleaf3 C: z-treecdrom zleaf.exe /name zleaf1 /size 900x700 /Language Nihongo C: z-treecdrom zleaf.exe /name zleaf3 /size 925x675 /Language Nihongo /name /size zleaf zleaf3 zleaf1 OK 16

17 3.2 z-tree CD ROM CD ROM z-treecdrom treatments C : CD-ROM z-tree Web 17

18 4 z-tree 1. File Open treatments pd z-tree Urs Fichbacher Web (

19 2. zleaf1 zleaf2 3. z-tree 2 z-leaf Run Clients Table z-leaf 13 Clients Table z-leaf 4. Background 14 Background 15 General Parameters Number of subjects Number of Groups 2 paying periods OK 15 General Parameters 19

20 5. Run Start Treatment (1) 17 (2) OK z-leaf 2 z-leaf z-leaf [Alt]+[Tab] 20

21 2 z-leaf z-tree 2 z-leaf [ ] z-tree z-leaf Alt F4 z-tree z-tree z-leaf 1 xls 2 Decision 21

22 Treatment Language Nihongo File New Treatment 4 pd pd 18 1 pd Decision Profit display 1. Decision 2. Edit Copy Edit Paste Decision Profit display. Background Decision [+] pd [+] pd Globals.do 22

19 2 1. Globals.do 2. Edit Copy 3. session 4.

23 Globals.do 2. Edit Copy 3. session 4. Edit Paste 20 3 File Save As Period Verbleibende Zeit Decision [+] Active Screen Waiting Screen Active Screen Waiting Screen Active Screen 23

24 Standard Box Standard Box 21 4 Standard Your decision Label Your decision(1=cooperate 2=defect) 1=, 2= 22 5 Profit display [+] Label 23 z-leaf Background 24

25 (Stag Hunt Game)

26 24 25 Pi 4 PiCD= C D C D 26 // 27 26

27 27 Battle of Sex

29 6 2 A B A 1000 B B A A B =1000 A A ( ) 28 0 u(0) 2000 u(2000) u(0) u(2000) 0.5 B 0.5u(0)+0.5u(2000) 1000 u(1000) 6.1 z-tree 29

30 Untitled Treatment 1 Treatment Language Nihongo File New Treatment OK -Tree New Treatment 6.2 A B 1. Background session Treatment New Program 2. A LotA, B LotBWin, LotBLose Program LotA=1000; LotBWin=2000; LotBLose=0; 3. OK LotA LotBWin,LotBlose A B 1. Background Treatment NewStage name InputDecision 30

31 : NewStage Background Background Background Background Stage Background Stage Background Stage Background Stage Stage Stage 2. Active screen Treatment New Box Standard Box Standard box -Tree Standard Box 3. Standard box Treatment New Item 29 Label Label Treatment New Item Input 30 Variable Layout Input Decision Decision

30 Minimum Maximum 5. Treatment New Button OK 6.

32 30 Minimum Maximum 5. Treatment New Button OK Input Decision Treatment New Stage ShowResults Treatment New Program LotBWin 0.5 LotBLose z-tree random() 0 1 Random Random random(); 32

33 0.5 B LotBWin, LotBLose if if if( ) else ; else 1 if B B LotB if (Random>0.5) LotB=LotBWin; else LotB=LotBLose; Random 0.5 LotB LotBWin LotBLose A (Profit) LotA B LotB OK if (Decision==1) Profit=LotA; else Profit=LotB; Decision== 2 if Decision Profit LotA LotB if,, 2 ; 2 Profit=if(Decision==1,LotA,LotB); Active screen Standard box Item 6.3 Input Label Variable Profit Layout 31 z-leaf 33

34 OK syntax error; ; = == <= >= =< => 34

35 Unknown variabler; XXXXXX XXXXX XXXXXX=10; Decision Desicion Profit profit LotBWin LotbWin // z-tree z-tree Random=round(random()*100,0)+1; //random from 1 to 100 if (Random>Boundary) { Lot2=Lot2Win; } elese { Lot2=Lot2Lose; } Profit=if(Input==1,Lot1,Lot2); // 1 Random 2 1 Random=round(random()*100,0)+1; //random from 1 to 100 if (Random>Boundary) { Lot2=Lot2Win; } elese { Lot2=Lot2Lose; } //Profit=if(Input==1,Lot1,Lot2); 35

36 OK 2 Random=round(random()*100,0)+1; //random from 1 to 100 //if (Random>Boundary) { Lot2=Lot2Win; } elese { Lot2=Lot2Lose; } //Profit=if(Input==1,Lot1,Lot2); else elese X Y X Y X LotB1=0 LotB2=5000 LotB3=1000 Random 0.01 LotB LotB LotB3 3 1 if 3 if (Random<=0.01){LotB=LotB1;} if (Random>0.01 & Random<=0.11){LotB=LotB2;} if (Random>0.11){LotB=LotB3;} = = 2 if Y LotA LotB X Y X Y InputDecisionX InputDecisionY 36

37 X DecisionX Y DecisionY ProfitDisplay X ProfitX Y ProfitY Profit ProfitX+ProfitY 37

38 7 z-tree , ,500 1, (8 ) 1500 ( ) 1 8 1, ,000 8, ,500=60, , , , , z-tree 42 38

39 7.1 5 z-tree z-tree z-tree z-leaf 1 z-leaf z-tree Subject Subject 2 z-leaf Subject 3 4 A B z-leaf Run Client s Table 2 BackGround Number of groups Group Group Num_of_Group= Grobal Group=rounddown((Subject-1)/ Num_of_Group, 1)+1; 39

40 6 2 Subject Subject Subject Num of Group (Subject-1)/ Num of Group rounddown(, ) rounddown((subject-1)/ Num of Group, 1) rounddown((subject-1)/ Num of Group, 1) Group Num_of_Group=5; 7.2 Contribution Contribution Table Function sum( ) find( ) count( ) maximum( ) minimum( ) 5 Contribution SumC SumC=sum(Group==:Group, Contribution); 40

41 Group Group Group Group Group sum(group==:group, Contribution) z-tree Contribution SumC DOKECHI DOKECHI=minimum (Group==:Group, Contribution); DOKECHI same( ) treatments same( ) SumC=sum(same(Group), Contribution); N=count(same(Group)); N same( ) N=count(Group==:Group); OthersDecision OthersDecision = find( same ( Group) & not ( same ( Subject ) ), Decision); Decison OthersDecision same OthersDecision = find( Group==:Group & Subject<>:Subject, Decision);

8 1 3 4 3 2 3 3 7 z-tree 8.1 8.1.1 34 Subjects Globals 8.1.2

43 z-tree Subjects Globals Subjects Subjects z-tree z-tree Score Score Score Subjects 43

44 8.1.3 Globals Globals Session Subjects Globals 1 Session Session Subjects, Globals, Session Subjects 3 Globals Session

45 Subjects Globals Session Subjects Globals Session Subjcet Globals Session 1 Subject, Globals, Session Subject, Globals, Session 1 Subject, Globals Subject, Globals Session 2 2 Subejct, Globals, Session 6 (Summary Subjects Globals Session Summary z-tree z-tree z-tree

8.2.3 2 Globals Subjects Session Globals Subjects Subjects Subjects Contribution Globals MaxContribution Globals MaxContribution=subjects.

46 Globals Subjects Session Globals Subjects Subjects Subjects Contribution Globals MaxContribution Globals MaxContribution=subjects.max(Conrtibution); z-tree Subjects Contirubution MaxContribution Globals DecOfOne (Subject) Decision DecOfOne=subject.find(Subject==1,Decision); z-tree Subjects Subject 1 Decision RepDec Subjects Globals 46

47 Globals Subjects 8.3 Summary Summary z-tree Globals Session Subjects Summary Summary Summary 8.4 Parameters z-tree Subjects Globals Parameter Parametesr 37 S1, S2, S3 1,2,3 37 Parameter Role Parameters Period Parameters Specific Parameters Parameters Subjects Globals

48 Role Period Specific Subjects Globals Subjects Role Specific Role Role Specific Role Specific 7 48

49 * / < <= ==!= >= > & and or : / x=y; if (a) {ss} if (a) {ss1} else {ss2} if (a) {ss0} elsif (b1) {ss1} elsif (b2) {ss2}... t.do{ss} a ss a ss1 ss2 a ss0 b1 ss1 t ss 49

50 if (a) {ss0} elsif (b1) {ss1} elsif (b2) {ss2}... else {sse} t.new{ss} array v[x]; array v[x, y]; array v[x, y, z]; while(a) {ss} repeat {ss} while (a); later (a) do {ss} later (a) repeat {ss} a ss0 b1 ss1 sse t ss ( ) v[x] 1 1 x ( ) v[x] 1 x y ( ) v[x] z x y a ss <Ctrl><Alt> [F5] ss a a ss <Ctrl><Alt> [F5] a ss ss a ss ss a ss a abs(x) and(a, b) atan(x) cos(x) exp(x) gettime() if(a, x, y) ln(x) log(x) max(x, y) min(x, y) mod(x, y) not(a) or(a) pi() x a b True x x x a x y x x 10 x,y x,y x a a b 50

51 power(x, y) random() randomgauss() randompoisson(x) round(x, y) rounddown(x, y) same(x) sin(x) sqrt(x) x x y y x x y x y x y x == :x x x average(x), average(a, x) count(), count(a) find(x), find(a, x) maximum(x), maximum(a,x) median(x), median(a, x) minimum(x), minimum(a, x) product(x), product(a, x) regressionslope(x, y) regressionslope(a, x, y) stddev(x), stddev(a, x) x a x a x x a x x a x x a x x a x x a x x y a x y x a x 51

52 : p : p

54 (5 0) = ((5 3) = (5 4) = ((5 1) =

55 OK 0 5 OK OK OK 55

56 (1) (5) (1)

57 2. (4) ),2) 50,100,150, (5) (4) (3) 57

58 =

59 (1) (5) (1) (2) (4) 59

60 (5) ),2) 200,150,100, (4) (5) (3) =10 60

61 61

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