Analyzing Human Negotiation using Automated Cognitive Behavior Analysis: The Effect of Personality. Pedro Sequeira & Stacy Marsella

Transcription

1 Analyzing Human Negotiation using Automated Cognitive Behavior Analysis: The Effect of Personality Pedro Sequeira & Stacy Marsella

2 Outline Introduction Methodology Results Summary & Conclusions

3 Outline Introduction Methodology Results Summary & Conclusions

4 Automated Cognitive Behavior Analysis Goals Gain insight on how people process information Uncover underlying psychological structures responsible for overt behavior Questions that ACBA allows addressing Based on which task information do people make their decisions? What are the temporal dynamics of behavior? For the same task, can we identify behavioral patterns across individuals? Can we track the development of the structures over time?

5 ACBA Approach Humans as information-processing systems [Miller et al. 1960, Newell 1994, Simon 1978] Genetic Programming (GP) Idea: generate candidate programs explaining behavior Programs are combination of relevant symbols about the task and certain operators receptors Knowledge Behavior structures effectors Decisionmaking Human behavior Fitness is measured by how consistent the output is in relation to the behavior outcomes state Task Environment observed result / outcome GP progressively generates better candidates Most fit programs are chosen as hypotheses of, i.e., possible solutions for, the underlying structure of the observed behavior observed state features Symbols / Terminals Genetic Program Decisionmaking Computer output consistency measure / fitness

6 ACBA GP Post-processing Challenges Identify the underlying invariant behavioral structures Identify behavioral phenomenon of interest e.g., across individuals or across tasks How to process solutions stemming from GP? Approach Set of computational tools or components Each analyzes observed behaviors according to different criteria Each provides different insights on the underlying cognitive behavioral structures

7 Outline Introduction Methodology Results Summary & Conclusions

8 ACBA Methodology collect behavioral data discover behavioral structures analyze structural patterns Human Human Characteristics Analysis datapoints Behavior Data-set Frequency Analysis Human Humans Human Computer for each data-point Genetic Programming candidate programs Data-set solution programs Robustness Analysis Data-point Grouping Group Robustness Analysis Collection Grouping Groups Data-set Groups Characteristics Analysis Frequent- Pattern Mining

9 Scenario Investigate human negotiation behavior Cognitive social task Turn-based, multi-level, multi-issue bargaining setting [Pruitt and Lewis, 1975] 2 negotiators negotiate sequentially over: 3 records 2 lamps 1 painting

10 collect behavioral data ACBA Data Collection Human Human datapoints Behavior Data-set Human Humans Human Computer

11 Human Negotiation Data Use negotiation data collected in [Xu et. al, 2017] 405 subjects negotiating against fixed intermediate strategy Collected information on subject s personality Social value orientation (SVO) [Murphy et al., 2011] Machiavellianism (Mach) [Christie & Geis, 2013] Idea Use ACBA to understand the behavior of the human negotiator Capture the variability of negotiation behavior according to the subjects personality Can ACBA confirm the behavior predispositions attributed to different personality traits?

12 Behavioral Data Collection Negotiation scenario Each offer o k O at each round k = 1,..., K is a full partition over the items # # # O is the set of all possible partitions between the items, hence O = 24 Example of data-point d: O h = O a = o 1 o 2 o 3 o K A behavior data-set D comprises several data-points

13 Payoffs Each item has associated a certain payoff value Record is worth 11 Lamp is worth 5 Painting is worth 2 Therefore, each data-point has also two payoff sequences O h = O a = o 1 o 2 o 3 o K Payoff sequences P o h = 28, 32, 35,, 28 P o a = 35, 23, 29,, 23

14 ACBA Genetic Programming collect behavioral data discover behavioral structures Human Human Human Humans datapoints Behavior Data-set for each data-point Genetic Programming Data-set Human Computer candidate programs solution programs Collection

15 Genetic Programming Approach Search in a space of expressions for programs that best fit human negotiator s behavior Predict human negotiator s offer sequence O h = o h h 1,, o K Programs Syntax trees, nodes correspond to primitives taken from F or T Set of functions F = {(x + y), (x y), (x y), (x y), max(x, y), min(x, y), (x^y), (x? y: z: w)} T = C V are the terminals Constants: C = {0,1,2,3,5,6,7,11,23,34,45} V are variables encoding the observable state features at each round k = 2,, K

16 Genetic Programming for each data-point d Behavior Data-set Population of Programs for each program p new population Fitness Function F d p Selection Crossover Mutation new population Population of Programs for each program p Fitness Function F d p Selection Crossover Mutation new population Population of Programs for each program p Fitness Function F d p Selection Crossover Mutation

17 Fitness Function Calculate fitness of a program p regarding a data-point d: F d p = K k=2 V d k h p P o k 2 /K Negative RMSE between program s output and payoff observed from the negotiator s offers programs Programs attaining maximal fitness for a data-point Occam s razor principle Select from the existing hypotheses the ones with the fewest assumptions, i.e., shorter programs

18 Outline Introduction Methodology Results Summary & Conclusions

19 collect behavioral data Methodology discover behavioral structures analyze structural patterns Human Human Characteristics Analysis datapoints Behavior Data-set Frequency Analysis Human Humans Human Computer for each data-point Genetic Programming candidate programs Data-set solution programs Robustness Analysis Data-point Grouping Group Robustness Analysis Collection Grouping Groups Data-set Groups Characteristics Analysis Frequent- Pattern Mining

20 Characteristics Analysis Gather statistical information for solutions of groups of points Results First indication that proselfs may have more complex underlying strategies Consistent with [de Dreu & van Lange, 1995] count length Num. unique variables

21 Frequency Analysis Counts the frequency of solution programs and of their sub-programs for a set of data-points Proselfs: (InitOffPayoff/OffPayoff) Ratio between initial offer and current offer by the opponent Related to anchoring effect of first offers in negotiation [Galinsky & Mussweiler, 2001] Used as a scaling factor for the proposal, in line with [Murphy et al., 2011] E.g., max(offpayoff,(initproppayoff*(initoffpayoff/offpayoff))) Prosocials: (34-OffPayoff) Difference between a high payoff and the opponent s offered payoff Used to establish a minimum acceptable payoff, related with BATNA [Galinsky & Mussweiler, 2001] E.g., max(23,(34-offpayoff))

22 Robustness Analysis Assesses the robustness of solution programs and their sub-programs Robustness is mean fitness across a collection of data-points Proselfs:(24-PropPaintings) Either 24 or 23 Almost constant strategy proposing offers whose payoff is in [23,24] Disregards opponent s offers, denoting non-conceding nature of pro-selfs [de Dreu & van Lange, 1995] Prosocials: (18+(PropPayoff/6)) Slowly increasing proposal On average, the maximum target payoff is right below fairness Corresponds to highly-cooperative strategy [de Dreu & van Lange, 1995; Murphy et al., 2011]

23 Outline Introduction Methodology Results Summary & Conclusions

24 Summary Automated Cognitive Behavior Analysis Study the underlying structure of human behavior Genetic programming iteratively generates candidate programs capable of explaining behavior Implementation of different tools to aggregate the solutions relative to different data partitions Applied ACBA to human-agent negotiation data-set ACBA could uncover distinct behavioral structures in solutions for different trait groups Results are consistent with behavioral trends as described in the literature s have into account task information and lead to outcomes consistent with personality traits

25 Ongoing Work clustering Based on terminal similarity, expression similarity, tree-edit similarity Use agglomerative nesting clustering [Kaufman and Rousseeuw, 2005, Ch. 5] grouping Group solution programs from a set of data-points according to their semantic similarity Allow the identification of semantic families of solutions Data-point grouping Based on the similarity of their solution programs Identify behavior instances resulting in similar strategies and behavioral trends

26 Future Work The effect of dyads in negotiation Use ACBA to study role of opponent in framing negotiation Group by opponent characteristic and analyze underlying behavior structures Apprenticeship learning using inverse reinforcement learning Use ACBA to recover expert strategy given demonstrated behavior Does not assume prior structure or relationship (e.g., linear) between features Apply ACBA to different domains E.g., problem-solving tasks Validate ACBA by comparing with outputs from TA and CTA protocols

27 Questions? More info: web.northeastern.edu/cesar/psequeira/acba

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