《实验经济学II》第一阶段课程安排.pdf
《实验经济学II》第一阶段课程安排.pdf
6/9/2014 Lecture 3 Bioeconomics of Individual Decision Making HTR2A SHUFE Soft vs Hard Decision Theory Utilitarism – Early Days … Psychophysics of diminishing sensitivity applied to utility theory • Utilitarianism since Bentham • Principle of diminishing marginal utility • Cardinality? Utilitarism • Utilitarianism since Bentham • Principle of diminishing marginal utility • Modelling risk attitude … Modelling Risk Taking • Bernoulli (1728) described a St Petersburg lottery which pays 2n dollars if in a sequence of coin tosses, Head first appears on the nth trial. – Notice that this lottery has infinite expected payoff since it pays 2n dollars with 1/2n probability. – At the same time, 1/2n is the chance of winning more than 2n! • This St Petersburg Paradox demonstrates that expected payoff does not provide a “good” criterion for assessing the worth of a lottery. 1 6/9/2014 Utilitarianism Modelling Risk Taking • Bernoulli (1728) then suggested that the incremental “moral worth” of money x should be inversely proportional to x leading him to offer a logarithmic function for his moral worth function. • We can check that this solves the St Petersburg paradox • Interestingly, Bernoulli is a contemporary of Ernst Heinrich Weber (1795–1878) Weber. Sometimes, great minds do think alike … • Utilitarianism since Bentham • Principle of diminishing marginal utility • Modelling risk attitude a bit later … • Desperately seeking cardinality … Dopamine (DA) and gain Dopamine and Reward • Reward as well as reward prediction errors (Schultz, Dayan, and Montague, 1997) • Novelty seeking (Cloninger, 1986) • Expected reward (Preuschoff, Bossarts and Quartz, 2005) Less about loss • Does not produce negative prediction error (Fiorillo, Tobler, and Schultz, 2003). • Administration of DA drugs affects risky decision making under gains but not under losses (Pessiglione et al 2006) Schultz, Dayan & Montague (1997) A Neural Substrate of Prediction and Reward. Science 9 Cardinal Utility at last …? Biological Bound • Utilitarianism since Bentham • Principle of diminishing marginal utility • Modelling risk attitude a bit later … • We now have a biology-based utility which delivers inter alia cardinality • Biological constraint in the occupancy of DA receptors (Berns, Capra, & Noussair 2007) • This delivers a neurochemical model of diminishing marginal utility coinciding with the psychophysics of diminishing sensitivity towards changes in utility 11 10 11 2 6/9/2014 Our Biological Tone Hypothesis Neurochemical Model of Diminishing Marginal Utility • Tone: low-level background firings • Higher DA tone, lower capacity, more concave in gain • Testable implication – greater risk aversion • Biological constraint in the occupancy of DA receptors (Berns, Capra, & Noussair 2007) Utility/ DA responds Utility/ DA responds DA Bound DA Bound less concave less concave gain gain 13 status quo lower DA tone 13 status quo 14 lower DA tone 14 Serotonin (5HT) and Loss Neurochemical Approach to Reference Dependence • Contributes to the regulation of aversive behavior • Harm avoidance (Cloninger, 1986) • Anxiety-related personality traits (Lesch et al, 1996) • Loss-gain framing (Roiser et al, 2009) • DA and 5HT Opponent Partnership Hypothesis • Kahneman and Tversky (1979) observed that economic outcomes tend to be perceived as changes, i.e., gains and losses, with respect to the status quo. • Apply Weber’s law, they conclude that the utility function would remain concave over gains but convex over losses, with the immediate implication of a tendency towards being risk seeking over loss oriented risks! • Opponency between reward and punishment in learning is fundamentally asymmetric (Daw, et al, 2002; Dayan and Huys, 2009) 15 Neurochemical Model of Reference Dependence Biological Tone Hypothesis for Utility over Losses • Biological constraint in the occupancy of DA receptors (Berns, Capra, & Noussair 2007) • DA and 5HT are in limited supply in the brain, they lead naturally to bounds to the value function in both gains and loss domains. (Zhong et al. 2009b) • This delivers a neurochemical model of referencedependent utility with tone serving as embodiment of status quo. • Tone: low-level background firings • Bound: limited availability • Higher 5HT tone, lower capacity, more convex in loss lower 5HT tone status quo Loss Less convex 17 17 18 5HT Bound Utility/ 5HT responds 18 3 6/9/2014 Dual System Hypothesis • Higher DA (5HT) tone associates with a more concave (convex) valuation function over gains (losses). utility less concave lower DA tone Test of the Implications of Dual-System Hypothesis loss status quo lower 5HT tone gain less convex • Biological basis for the psychophysics of valuation sensitivity • 19implication re “kink” at status quo 19 Findings Support Tone Model of Reference Dependence Corroborating Dual System Hypothesis (Zhong et al., 2009 b) • Assess risk attitude over gains and losses • Candidate Gene – Dopamine transporter DAT • Midbrain activation (Schott et al., 2006) • In vivo transporter availability (van Dyck et al., 2005) • Tone: 9 ↓, 10 ↑) • Candidate Gene – Serotonin transporter • Tone: 5HTTLPR – short ↑, long ↓; STiN2: 10 ↑, 12 ↓ Figure 2. (a) Risk attitude over gains and DAT1. Subjects with the 9-repeat allele (low DA tone) are more risk-tolerant than subjects with the 10repeat allele (high DA tone). (b) Risk attitude over losses and STin2. Subjects with the 10-repeat allele (high 5HT tone) are more risk-tolerant than subjects with the 12-repeat allele (low 5HT tone). (c) Risk attitude over losses and 5-HTTLPR. Subjects with the long allele (high 5HT tone) are nominally more risk-tolerant than subjects with the short allele (low 5HT tone), though this relation is not statistically significant. 21 Findings Support Tone Model of Reference Dependence Risk attitude over gain Risk attitude over loss pvalue Gene OR CI DA DAT1 1.77 1.04 3.04 0.035 5HT STin2 1.22 0.96 1.54 0.104 5HT 5HTTLPR 1.21 0.86 1.68 0.264 DA DAT1 1.63 0.88 2.99 0.118 5HT STin2 1.36 1.03 1.79 0.029 5HT 5HTTLPR 1.36 0.97 1.9 22 Large Scale Study with Biologically Enriched Choice Data • 3433 subjects: 1640 are males, mean age 21.9 years (SD = 2.3) for males and 20.8 (SD=2.4) for females • 2191 from Singapore: 46.1% males • 1242 from Beijing: 51.7% males • All undergraduate Han Chinese • Each contributed blood/saliva sample • Average compensation of RMB400 0.075 23 4 6/9/2014 Introduction Dopamine D4 Receptor Gene Exon III Polymorphism Predicts Curvature of the Utility Function in Han Chinese • Dopamine D4 Receptor ( DRD4) gene - located in chromosome 11 - a particular variant: the exon III VNTR polymophism Yushi Jiang, Mikhail Monakhov, Songfa Zhong, Richard P. Ebstein and Soo Hong Chew - 2-11 repeats among human - most common: 4R - second most common: 7R (in Caucasians) & 2R (in Asians) Over 90% Source: Jiang, Chew, & Ebstein, (2013) Financial Investment Task Introduction • Motivation - Recently several studies with small samples of Caucasian subjects offer preliminary evidence that certain alleles of dopamine D4 receptor (DRD4) exon III polymorphism may be associated with a greater propensity to take financial risk (Dreber et al, 2009; Kuhnen and Chiao, 2009). • Endowment: S$27 •Investment amount: X (choose among 10 options) • Total earnings: 50%: S$27+1.5X 50%: S$27-X Tone-based Hypothesis Literature • Dopamine transporter (DAT) gene predicts economic risk attitude (Zhong et al., 2009) • Association was reported between DRD4 7R and risk preference among Caucasian samples - Studies of drug addiction indicate that low DA tone corresponds to higher sensitivity to reward (Volkow 2012) - 4/4R: most efficient genotype (Asghari et al. 1995), higher tone; non-4/4R: reduced striatal DRD4 availability, and thus lower tone - 7R carriers are less risk averse gender effect: association is seen only in males Not always replicated - Hypothesize that 2R carriers in East Asian populations 5 6/9/2014 Experiments • Subjects were recruited via email advertisement from NUS and various universities in Beijing • Procedure: - incentivized laboratory-based economic experiments; - biological material samples (blood or saliva); - online surveys including personality questionnaires. Experiments Experiments • 3433 subjects: 1640 are males, mean age 21.9 years (SD = 2.3) for males and 20.8 (SD=2.4) for females - 2191 from Singapore: 46.1% males - 1242 from Beijing: 51.7% males - all undergraduates - Han Chinese • DRD4 VNTR genotype: - Grouping scheme: 4/4R vs all others (non-4/4R) - Around 59% of the subjects are 4/4R carriers Even-Chance lottery • Incentivized behavioral tasks: - Financial investment - Even-chance lottery - Sure-bet task • Personality questionnaires: - NEO Personality Inventory (NEO-PI) - Temperament and Character Inventory Revised (TCI-R) Switching point = indicator of risk attitude Sure-bet Task Comparison of Tasks • Lottery tasks (Even-chance lottery & Sure-bet): more primitive (1 bird in hand vs 2 in the tree) distinguish risk neutral and risk seeking • Investment task: more complex (“investor" optimizing on mix between safe and risky assets) 10 - Switching point = indicator of risk attitude 6 6/9/2014 Correlation across Behavioral Tasks Even-chance Sure-bet Risk Attitude Comparison across Genotypes p=0.013 p=0.053 p=0.003 Financial investment Even-chance lottery Sure-bet 6 Investment 5 Even-chance 1 Sure-bet 0.311* 1 Investment 0.282* 0.309* 4 3 2 1 1 Spearman’s rho values. *p<0.001 0 4R/4R Subjects tend to demonstrate consistent risk attitudes across the tasks. Association between DRD4 Exon III VNTR and Risk Attitude in Separate Tasks (OLR) Model-free OLR analysis • Ordered Logit Regression (OLR) - Financial investment: the choice number (ranging from 1-10) of investment composition is used as dependent variable (DV); - Even-chance lottery: the Switching Point (SP: ranging from 0-10) from lottery to the sure amount is DV; - Sure-bet task: 10 – SP (ranging from 0-10) from the sure amount to lottery is DV => larger number indicates higher risk tolerance. Financial investment 1 (a) 1 (b) 1 (c) Computational model Age Father’s edu. Siblings Family inc. Log likelihood Observations where X is the vector of observed individual characteristics & environmental factors • MLE: - combining all 3 tasks - estimate coefficient ρ 3 (c) The proportional Odds Ratios from OLR are presented. Robust Standard Errors are in parentheses. *p< 0.10, **p< 0.05, ***p< 0.01; DRD4=0 if participant carries the 4R/4R genotype, 1 otherwise. Association between Risk Attitude and DRD4 exon III VNTR: Computational Method (a) (b) (c) -0.001*** (0.0002) -0.001*** (0.0003) -0.001*** (0.0003) 0.002*** (0.0003) 0.001*** (0.0003) 0.000 (0.0001) 0.002*** (0.0003) 0.001*** (0.0003) 0.000 (0.0001) -0.000 (0. 0001) 0.000 (0. 0001) 0.000* (0. 0001) 0.000 (0. 0001) 0.010*** (0.0015) 0.012 0.0011 0.029*** -36014.337 59379 3020 Female City Age Father edu • Parameter for risk preference: Sure-bet 3 (b) 0.656*** 0.680*** 0.678*** 0.876** 0.888* (0.044) (0.045) (0. 047) (0.057) (0.061) 1.154** 0.706*** 0.689*** 1.148** 1.127* (0.078) (0.045) (0. 048) (0.074) (0.078) 1.011 1.006 0.999 0.993 0.993 (0.013) (0.013) (0.013) (0.014) (0.014) 1.006 0.985 1.045 (0.028) (0.027) (0.029) 1.012 1.020 0.975 (0.030) (0. 030) (0.029) 1.010 0.970 0.959 (0.031) (0.028) (0.028) 0.997 0.978 0.981 (0.017) (0.016) (0.017) -6934.141 -6746.406 -6072.710 -6587.755 -6396.202 -5757.415 -6731.134 -6578.787 -5933.892 3419 3341 3009 3147 3071 2765 3266 3189 2872 City DRD4 Non-4R/4R where ρ > 0 is the Arrow-Pratt measure of risk aversion: higher ρ more risk averse 3 (a) 0.656*** (0.041) 1.133** (0.071) 1.007 (0.013) Female ρ • Exponential utility function: Even-chance lottery 2 (a) 2 (b) 2 (c) 1.144** 1.144** 1.128* 1.129* 1.152** 1.164** 1.210*** 1.212*** 1.201*** (0.070) (0.070) (0.074) (0.072) (0.074) (0.079) (0.076) (0.077) (0.081) DRD4 Mother’s edu. - Three estimation models for each task: (a) only DRD4 (b) DRD4 & individual characteristics (sex, city, and age) (c) DRD4 & individual characteristics &environmental factors (parents’ education, number of siblings, family income level) non-4R/4R Mother edu Siblings Income level Constant Avg. ρ (Std. dev.) Noise Log likelihood Observations Clusters 0.012*** (0.0002) 0.012 0.0004 0.029*** -41203.410 67549 3432 0.011*** (0.0014) 0.012 0.0011 0.029*** -40084.986 65941 3353 Clustered standard errors are in parentheses: * p < 0.10, ** p < 0.05, *** p < 0.01; The DRD4 exon III VNTR is coded as 4/4 genotype=0, otherwise=1. 7 6/9/2014 Computational Model: An Illustration Correlation between Risk Attitude and Personality Traits Financial investment DRD4 exon III VNTR Coeff. Std Err. P-value NEO-PI-R Neuroticism Extraversion Openness to experience Agreeableness Conscientiousness -1.2851 -0.1357 -0.0077 -0.0363 0.2701 0.8611 0.7722 0.6384 0.6117 0.7633 0.136 0.861 0.990 0.953 0.723 TCI-R 0.2286 0.5532 0.679 Novelty seeking -0.2762 0.7253 0.703 Harm avoidance -0.4549 0.5667 0.422 Reward dependence -0.0083 0.6983 0.990 Persistence -0.5798 0.6782 0.393 Self-directedness -0.1843 0.6065 0.761 Cooperativeness 0.4438 0.5477 0.418 Self-transcendence Scores of each personality scales were used as dependent variable in separated linear regressions; DRD4 = 0 if participant carries the 4R/4R genotype, 1 otherwise. All regressions and estimations controlled for sex, city, and age. Peripheral Serotonin Receptor 2A (HTR2A) Gene Expression and Loss Aversion Yunfeng LU, Mikhail Monakhov, Poh San LAI, Soo Hong CHEW, Richard P. Ebstein Sure-bet Odds Ratio. Std. Err. Odds Ratio. Std. Err. Odds Ratio. Std. Err. NEO-PI-R Neuroticism Extraversion Openness to experience Agreeableness Conscientiousness 0.9940*** 1.0053*** 1.0068** 0.9952** 1.0012 0.0018 0.0019 0.0024 0.0024 0.0019 0.9958** 1.0084*** 1.0078*** 1.0005 1.0017 0.0017 0.0020 0.0078 0.0026 0.0019 0.9987 1.0035* 1.0034 0.9997 1.0018 0.0018 0.0019 0.0023 0.0023 0.0018 TCI-R Novelty seeking Harm avoidance Reward dependence Persistence Self-directedness Cooperativeness Self-transcendence 1.0114*** 0.9916** 0.9962 1.0043 1.0003 0.9975 0.9963 0.0031 0.0026 0.0034 0.0027 0.0027 0.0029 0.0034 1.0170*** 0.9910*** 1.0078** 1.0060** 1.0007 0.9998 0.9994 0.0034 0.0027 0.0032 0.0025 0.0028 0.0029 0.0033 1.0065** 0.9945** 1.0011 1.0045 0.9995 0.9999 0.9978 0.0033 0.0025 0.0033 0.0025 0.0026 0.0029 0.0035 * p< 0.10, ** p< 0.05, *** p< 0.01 Association between DRD4 Exon III VNTR and Personality Traits Risk Attitude Even-chance lottery Summary • 4R allele of DRD4 exon III VNTR is linked with higher risk aversion than the minor alleles in Han Chinese. • Association between DRD4 and risk attitude is not only observed with financial investment tasks, but can also be extended to more natural contexts. • No significant association between DRD4 and relevant personality traits. • For certain genes, e.g., serotonin receptor 2A (HTR2A), the level of mRNA measured in blood can serve as proxy for gene expression (Kohane & Valtchinov, 2012; Rollins, Martin, Morgan, & Vawter, 2010) – reflects genomic and epigenetic factors • This approach does not work for DRD4 which is not expressed in blood. 8 6/9/2014 Structural Estimation of Value Function is parameter for loss aversion 5 Choice Tasks Hypothesis Serotonin Receptor (HTR2A) mRNA level in the blood relates positively to loss aversion. Behavioral x Biological Economics and the Social A1 = even-chance prospect A2 = even-chance hazard A3 = longshot prospect A4 = longshot hazard A5 = mixed hazard Sciences DECISION SHEET A2 This situation involves your guessing the color – red or black – of a card drawn randomly from a deck of 20 cards, comprising 10 black cards and 10 red cards. Option A: You guess the color – black or red – and then draw a card from the deck of 20 cards. If you make a correct guess, you lose $0; otherwise, you lose $15. That is: 50% chance of losing $15 and 50% chance of losing $0. Design The Option B column lists 10 loss amounts each corresponding to what you will lose for sure if you choose this option. (Notice that the loss amounts are displayed in a descending manner.) Descriptive Statistics • Subsample 205 based DECISION: Forof each of theSingapore 10 rows, please indicate yoursubjects decision in the final column with a tick ( ). • We adopt the price-list design Option A Option B Decision 1 50% of losing $15, 50% of losing $0 Losing $8.00 for sure A 2 50% of losing $15, 50% of losing $0 Losing $7.80 for sure A B 3 50% of losing $15, 50% of losing $0 Losing $7.60 for sure A B 4 50% of losing $15, 50% of losing $0 Losing $7.50 for sure A B 5 50% of losing $15, 50% of losing $0 Losing $7.40 for sure A B 6 50% of losing $15, 50% of losing $0 Losing $7.20 for sure A B 7 50% of losing $15, 50% of losing $0 Losing $7.00 for sure A B 8 50% of losing $15, 50% of losing $0 Losing $6.80 for sure A B 9 50% of losing $15, 50% of losing $0 Losing $6.60 for sure A B 10 50% of losing $15, 50% of losing $0 Losing $6.40 for sure A B B 5 9 6/9/2014 Patterns in Switching Point Main Results • Fourfold pattern of risk attitudes HTR2A and Neuroticism (NEO) HTR2A and Harm Avoidance (TCI) Conclusion • Hypothesis is supported – Higher peripheral HTR2A gene expression level is associated with greater degree of loss aversion. • Direction of association is consistent with previous findings on anxiety-related personality traits. • First finding linking gene expression in blood with financial risk attitude. Relating Telomere (“end-part”) Length with Temporal Discounting and Risk Diversification 10 6/9/2014 Telomere in Nutshell Human chromosomes (grey) capped by telomeres (white) Temporal Discounting Tomorrow 31 days later Decision 1 $100 $101 A☐B☐ 2 $100 $104 A☐B☐ 3 $100 $107 A☐B☐ 4 $100 $110 A☐B☐ 5 $100 $113 A☐B☐ 6 $100 $116 A☐B☐ 7 $100 $119 A☐B☐ 8 $100 $122 A☐B☐ 9 $100 $125 A☐B☐ 10 $100 $128 A☐B☐ Interaction between OXTR gene and Discounting on Telomere Length (Female Sample) o Individuals with the G allele are optimistic and empathetic; o The G allele would reduce the negative impact of impatience on ageing (Coefficient = 0.013, P < 0.05) o Involvement of OXTR accounts for gender difference Telomere in Nutshell • Cellular ageing – Each time a cell divides, some of the telomere is lost. When the telomere becomes too short, the chromosome reaches a "critical length" and can no longer replicate. This means that a cell becomes "old" and dies. • External factors may cause shortening of telomere length, such as cumulative life time stress, unhealthy lifestyle (smoking, alcohol abuse, insufficient sleep durations, etc) Discounting and Leukocyte telomere length o Discount rate is negatively correlated with telomere length (Coefficient = -0.005; P < 0.05) o The correlation is mainly driven by female sample (Coefficient = -0.009, P < 0.01) Risk Diversification in Portfolio Choice Task √ 1 2 3 4 5 6 7 8 9 10 Investment Cash $27.00 $24.00 $21.00 $18.00 $15.00 $12.00 $9.00 $6.00 $3.00 $0.00 Invest $0.00 $3.00 $6.00 $9.00 $12.00 $15.00 $18.00 $21.00 $24.00 $27.00 Total Earnings Correct Guess Incorrect Guess $27.00 $27.00 $31.50 $24.00 $36.00 $21.00 $40.50 $18.00 $45.00 $15.00 $49.50 $12.00 $54.00 $9.00 $58.50 $6.00 $63.00 $3.00 $67.50 $0.00 11 6/9/2014 Risk attitude and telomere length o Risk aversion is negatively correlated with telomere length in female sample (Coefficient = -0.0095, P < 0.05) Imaging Genetics of Familiarity Bias 6 Major Puzzles in International Macroeconomics: International Home Market Bias Is There a Common Cause? Obsfelt & Rogoff (2000, NBER) 1 0.9 0.8 Proportion of porfolio • Home-bias portfolio puzzle: Home investors prefer to hold home equity assets • Home-bias-in-trade puzzle: People have strong preference for consumption of home goods • Consumption correlations puzzle: Why isn't consumption more highly correlated across OECD countries? • Feldstein-Horioka puzzle: Current-account imbalances tend to be small relative to saving and investment when measured over any sustained period • Purchasing-power-parity puzzle: Half-life of real exchangerate innovations tend to be three to four years • Exchange-rate disconnect puzzle: Exchange rates are volatile and disconnected from fundamentals Canada Germany France UK Japan US 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 US Japan UK French & Poterba (1991) Familiarity breeds Investment Small Worlds: Huberman (2001, RFS) Shareholders of a Regional Bell Operating Company (RBOC) tend to live in the area which it serves, and an RBOC's customers tend to hold its shares rather than other RBOCs' equity. The geographic bias of the RBOC investors is closely related to the general tendency of households' portfolios to be concentrated, of employees' tendency to own their employers' stocks in their retirement accounts, and to the home country bias in the international arena. Together, these phenomena provide compeling evidence that people invest in the familiar while often ignoring the principles of portfolio theory. Modeling Preference among Sources of Uncertainty Chew and Sagi (2008, JET) • Introduce small worlds as homogeneous collections of events on which the decision maker behaves probabilistically • Provides foundation for modelling sourcedependent risk attitude. • Accounts for ambiguity aversion in terms of comparative risk attitude across small worlds. • Also, “Home Market Bias at Home” Coval and Moskowitz (2002, JF) 12 6/9/2014 Immediate Deliverable Enables modelling of source preference without worrying about whether you’d violate transitivity! different vNM utility functions to model distinct attitudes towards risks from different sources of uncertainty User Friendly Example … Cont’d User Friendly Example Consider a CRRA EU form: E(xr, Fs) where F refers to a probability distribution based on RV defined on a source of uncertainty s. Imaging Genetics Study of Familiarity Bias focusing on GABA Consider a source-dependent CRRA EU form: E(xr(s), Fs) • Can bound behavior of r(.), e.g.: – risk versus ambiguity, – strategic uncertainty, – familiarity GABA and Anxiety GABA and Anxiety • Major inhibitory neurotransmitter in nervous system acting as brake to prevent excitatory transmission, e.g., dopaminergic or serotoninergic, from reaching GABAergic-neuron rich regions • Evidence supports notion that dysfunction of GABAergic system contributes to anxiety (see review by Kalueff and Nutt, 1996) • Diazepam, e.g., Valium, as agonists for Type A GABA receptor (GABAA), used for anxiety disorders (Haefely, 1992; Sieghart, 1992) 13 6/9/2014 GABRB2 as Candidate Gene • We hypothesize that individual differences in the effectiveness of the GABAergic systems in coping with anxiety may explain differences in familiarity bias • GABRB2 is the β2 subunit gene forming the GABAA receptor sitting on chromosome 5 Gene-Brain-Decision Hypothesis Decision H3: Amygdala activation H1: GABRB2 genotype familiarity bias familiarity bias Gene Brain H2: GABRB2 genotype responsiveness of amygdala activation to unfamiliarity Study 1 establishes gene-behavior link • 325 Beijing based subjects • Betting on whether temperature is odd or even Beijing: RMB 11 Tokyo: RMB 13 Allele - Genotype 10 GABA SNPs Variation Minor allele Genotype dbSNP ID (M/m) frequency (%) Call rate z-scores p-values rs187269 T/C 19.25 322 2.80 0.005 rs1816072 T/C 40.40 323 2.47 0.014 rs252943 C/A 19.00 321 2.44 0.015 rs194072 T/C 16.40 317 2.40 0.016 rs1816071 A/G 27.19 320 2.31 0.021 rs252944 G/C 16.41 323 2.30 0.022 rs6556547 G/T 15.69 325 2.27 0.023 rs13178374 G/C 8.33 324 0.43 0.668 rs6891988 G/A 13.08 325 -0.23 0.815 Rs35351365 C/T 13.35 322 -0.05 0.958 B Genotype Association Probability of choosing Beijing decreases with increased presence of minor alleles (z = 2.64, p < 0.008, N = 325) • Allele – Minor allele (m) – Major allele (M) • Genotype – Homozygous minor (mm) – Heterozygous (mM) – Homozygous major (MM) Genetic Load Analysis 14 6/9/2014 Study 2 establishes G-B-D link with imaging • 37 subjects selected from the 325 subjects using most balanced SNP • Matched genotype – CC or CT: – TT: 22 15 (minor) (major) 5 secs • 80 trials (20 cities varying degree of familiarity) – Varying prizes and certainty amounts • Self-reported familiarity ratings of the 20 cities were collected after the scanning City Shanghai Hangzhou Tianjin Wuhan Chengdu Guangzhou Shenzhen Harbin Sanya Kunming Baotou Liuzhou Yibin Wuhu Jining Changde Golmud Jinzhou Yingtan Tongchuan In Chinese 上海 杭州 天津 武汉 成都 广州 深圳 哈尔滨 三亚 昆明 包头 柳州 宜宾 芜湖 济宁 常德 格尔木 锦州 鹰潭 铜川 1.5-2.5 secs