Experimental validation of how information influences individual behavior

Abstract

2. Tipos de Datos Recogidos En este experimento se recogerán los datos de las acciones tomadas por los participantes, sus pagos, tiempos por pantalla de decisión, así como datos sociodemográficos como edad, género, nivel educativo. En particular en este experimento también se recogen decisiones sobre aversión al riesgo, nivel cognitivo y preferencias pro-medioambientales. A continuación, se presenta el diccionario de variables de los datos recogidos donde se describe: variable, descripción de la variable, tipo de dato, valores posibles/formato. Diccionario de variables del Software Experimental Name Description Min Max Id_in_group Identifier within the group 1 60 pc_name Computer name (not collected online) - - pc_ip IP of the computer (not shown in the online) - - gRound Profits of the current round (in ECUs) - - gBlock1 Accumulated earnings from block 1 (in ECUs) - - gBlock2 Accumulated earnings from block 2 (in ECUs) - - gBelief Profits from the belief (in euros) - - gTotal Total earnings (in euros) - - gPay Total payable to subject (rounded and min 5) 5 - treatment tExtractive / tSocial / tApp / tFullInfo 1 = 0000 9 = 1000 17 = 2000 2 = 0001 10 = 1001 18 = 2001 3 = 0010 11 = 1010 19 = 2010 4 = 0011 12 = 1011 20 = 2011 5 = 0100 13 = 1100 21 = 2100 6 = 0101 14 = 1101 22 = 2101 7 = 0110 15 = 1110 23 = 2110 8 = 0111 16 = 1111 24 = 2111 1 24 tExtractive 0 = neutral 1 = extractive 2 = friendly 0 2 tSocial 0 = no social 1 = social 0 1 tApp 0 = no App 1 = App 0 1 tFullInfo 0 = no Full info 1 = Full info 0 1 q CONSTANT of the probability of being mostly A 0.5 0.5 t CONSTANT of the threshold from which the software knows the type of the subject 0.6 0.6 e CONSTANT of the probability that replaces p when the threshold is exceeded 0.1 0.1 c CONSTANT of the rate of change of the formula of p 1 1 p Probability that determines whether the object is A or B, H or L. Its initial value is 0.5. This value is not updated when the purchase is made so it stores the probability when the subject started the current period. 1 0 pBeliefAux Probability p of the current round after making the purchase. It is inverted if the subject's profile is B. pBelief Probability p of the finished block. It is inverted if the subject's profile is B. It is only used in round 31 to calculate the revealed index of the previous block. 1 0 TimesPurchaseA Times a has been purchased in current block 0 50 TimesBuyB Number of times that b has been bought in the current block 0 50 TimesRejectA Number of times a has been rejected in the current block 0 50 TimesRejectB Number of times b has been rejected in the current block 0 50 TimesAcceptCookie Number of times cookie has been accepted in current block 0 50 profile A = Mostly A profile in the current block B = Profile mostly B in the current block - - typeAB A = Subject type A in current round B = Subject type B in current round - - typeHL H = Subject type H in current round L = Subject type L in the current round - - offerAB a = Object offered in basket a in the current round b = Object offered basket b in the current round - - offerHL h = Object bid price h in the current round l = Object offered price l in the current round - - pCookie 0 = Subject has not opened the cookie description 1 = Subject has opened the cookie description 0 1 cookie 0 = The subject has not accepted the cookie 1 = Subject has accepted the cookie 0 1 purchase 0 = Subject has not purchased the object 1 = Subject has purchased the object 0 1 belief Value entered by the subject in the belief 0 100 pFormula 0 = Subject has not opened the formula description 1 = Subject has opened the formula description 0 1 revAux Percentage of the profile revealed by the subject after making the purchase. Revealed Percentage of the profile revealed by the subject. It is calculated in round 31 to calculate the belief and in round 60 to show the payments. 0 100 recomApp 0 = Subject has not used or does not recommend the App 1 = The user has used and recommends the app. 0 1 algorithm 0 = Subject has not agreed to use the App 1 = Subject has agreed to use the App 0 1 strategy 0 = Algorithm has not bought the object 1 = Algorithm has bought the object 0 1 rProfile* Random value to decide the subject profile 0 1 rTypeAB* Random value to decide the type of the subject 0 1 rTypeHL* Random value to decide the type of the subject 0 1 rOfferAB* Random value for deciding the type of the subject 0 1 rOfferHL* Random value to decide the type of the object 0 1 animation 0 = No animation of the bars will not be executed 1 = The animation of the bars is executed 0 1 question1-9 0 = False 1 = True Can only be passed if they answer correctly so all subjects will have: 011010110 0 1 * rProfile (random number between 1 and 0) If rProfile > q: profile = “A” If rProfile <= q: profile = “B” * rTypeAB (random number between 1 and 0) If profile == "A" and rTypeAB < 0.75: TypeAB = "A". If profile == "A" and rTypeAB >= 0.75: TypeAB = "B". If profile == "B" and rTypeAB < 0.75: TypeAB = "B". If profile == "B" and rTypeAB >= 0.75: TypeAB = "A". * rOfferAB (random number between 1 and 0) If rOfferAB < p: offerAB = "a". If rOfferAB >= p: offerAB = "b". * rOfferHL (random number between 1 and 0) It has 2 behaviors depending on whether or not the threshold has been activated and exceeded. - Without threshold: (0,4 >= p >= 0.6 ) # If "a" has been offered and you think it is "a" (offerAB == "a" and p > 0.5) # High probability of "h" (rOfferHL < p) # Low probability of "l" (rOfferHL >= p) # If "a" has been offered and you believe it is "b" (offerAB == "a" and p <= 0.5). # Low probability of "h" (rOfferHL < p) # High probability of "l" (rOfferHL >= p) # If "b" has been offered and you think it is "a" (offerAB == "b" and p > 0.5) # Low probability of "h" (rOfferHL > p) # High probability of "l" (rOfferHL <= p). # If "b" has been offered and you think it is "b" (offerAB == "b" and p <= 0.5) # High probability of "h" (rOfferHL > p) # Low probability of "l" (rOfferHL <= p). - With threshold: ((p > 0.6 o p < 0.4) and (tExtractive = 1)) # If "a" has been offered and you think it is "a" (offerAB == "a" and p > 0.5) # High probability of "h" (rOfferHL > 0.1) # Low probability of "l" (rOfferHL <= 0.1) # If "a" has been offered and you believe it is "b" (offerAB == "a" and p <= 0.5). # Low probability of "h" (rOfferHL < 0.1) # Alta probabilidad de “l” (rOfertaHL >= 0.1) # If "b" has been offered and you think it is "a" (offerAB == "b" and p > 0.5) # Low probability of "h" (rOfferHL < 0.1) # High probability of "l" (rOfferHL >= 0.1) # If "b" has been offered and you believe it to be "b" (offerAB == "b" and p > 0.5) # High probability of "h" (rOfferHL > 0.1) # Low probability of "l" (rOfferHL <= 0.1)This paper utilises (an observational approach within) a controlled laboratory experiment to investigate the nature of the privacy paradox. Participants are assigned a type exogenously and engage in online shopping to earn monetary rewards, while their shopping behavior is observed by an AI that aims to learn their type. Our findings indicate that participants willingly disclose significant amounts of private information, and persist in doing so even after receiving explicit information regarding the AI’s ability to learn about their type. However, we observe that the adoption of two mechanisms, namely “explainable AI” and a “privacy APP”, leads participants to adopt privacy-preserving shopping habits. Notably, this change in behavior occurs even in scenarios where the disclosure of private information has no impact on the monetary rewards. Our findings suggest that a plausible reason individuals share extensive personal information online stems from their lack of access to technologies enabling them to engage online while safeguarding their privacy. HIPOTESIS: Hypothesis 1: In our shopping environment, consumers do reveal large amounts of private information. Hypothesis 2: Participants keep revealing large amounts of private information even after they are made fully aware of the large amount of private information they are revealing, even in environments where consumers are aware that there are no monetary gains from trading private information. Hypothesis 3: In our shopping environment, the amount of private information revealed decreases when consumers are briefed about how the AI works (explainable AI). Hypothesis 4: In our shopping environment, the amount of private information revealed decreases when consumers are offered a privacy APP.MInisterio de Ciencia, Investigación y UniversidadContinene un fichero con las sesiones experimentales