Qualitative and Analogical modeling of cultural reasoning

1. Qualitative and Analogical modeling of cultural reasoning Kenneth D. Forbus Emmett Tomai Morteza Dehghani Qualitative Reasoning Group Northwestern University

2. Our overall approach Catalyze research by speeding encoding.Improve results by decreasing tailorability Eventually, practical modeling tools for analyst & decision-maker support Produce models via analogicalgeneralization, predictions via simulation Story Workbench(w/MIT) Interviews, surveys,& cultural stories collected Predicate calculusrepresentations ofstories, explanations Qualitative Concept Maps

3. Overview • Key ideas • Qualitative modeling • Analogical reasoning and learning • Practical natural language processing • Modeling cultural models of food webs • Qualitative models to capture content • Analogical modeling to gain insights, construct classifier • Modeling blame assignment (leave out, lack of time) • Qualitative model of attribution theory • Modeling moral decision-making • Sacred versus secular values represented via qualitative order of magnitude representations • Input representations via natural language

4. Qualitative Modeling • Formalizes intuitive knowledge of systems with continuous aspects • Levels of knowledge range from the person on the street to expert scientists and engineers • Has been used in wide range of scientific and engineering modeling • Design of mechanical, electrical, and hybrid systems, modeling ecosystems, modeling genetic regulation mechanisms • Has been used as formalism for human mental models • Cognitive modeling efforts, new educational systems • Offers useful level of precision for social science work • Responsibility Q- Coercion

5. Building Blocks for Analogical Processing Base SME Memory pool Probe Output = memory item + SME results CVmatch Target SME CVmatch SME CVmatch SME CVmatch Cheap, fast, non-structural • SME models analogical matching • Consistent with large body of psychological evidence • Has been used to make novel psychological predictions • Has been used in performance systems Match • SEQL models generalization from examples • Used to model several learning experiments • Used to make novel psychological predictions Generalize • MAC/FAC models similarity-based retrieval • Does not require hand-indexing of descriptions • Used to model several psychological experiments • Has been used in a performance system Retrieve

6. Most cognitive simulations have used hand-coded representations Problematic Tailorability Scaling up hard An alternative: Semi-automatic NL processing Simplified English eases parsing, semantic interpretation Practical NL Processing Hand translation or tagging As a result of a dam on a river, 20 species of fish are threatened with extinction. By opening the dam for a month each year, you can save these species, but 2 species downstream will become extinct because of the changing water level. Human Results Simulation Results

7. New Workflow Hand translation to simplified English Predicate calculus versions of stimuli, backgroundknowledge, stories… As a result of a dam on a river, 20 species of fish are threatened with extinction. By opening the dam for a month each year, you can save these species, but 2 species downstream will become extinct because of the changing water level. Because of a dam on a river, 20 species of fish will be extinct. You can save them by opening the dam. The opening would cause 2 species of fish to be extinct." Story Workbench: Semiautomaticpractical NLU using QRG-CEcontrolled language Human Results Simulation Results

8. The EA natural language system Parsing Sentence interpretation Discourse interpretation Task and domain specific reasoning Word-sense disambiguation Quantifier scoping Sentence attachment Semantic role assignment QP frame construction Situation reification Anaphora resolution Intra-sentential anaphora resolution Temporal ordering Semantic frames QP frame and process rules QRG-CEgrammar COMLEX Lexicon KB • Novel combination of off-the-shelf components: • ResearchCyc KB contents (1.2M facts) • Comlex lexicon • Allen’s parser • DRT-based semantic interpreter • Originally developed by Kuehne (2004) for modeling roles of qualitative representations in NL semantics

9. Cultural Models of Food Webs How groups conceive of relationships in the natural world Experiments carried out by Medin’s group: Participants given scenario about a perturbation to a population. Asked to predict effects Example: E: Do you think that the disappearance in the bears would affect other plants and animals in the forest? P: Well, there probably be a lot more berry growth for example, because they wouldn’t be eating the berries. There probably would be a lot less maybe dead trees, because they won’t be climbing on the trees and shredding them. […]

10. Qualitative Concept Maps QCM provides a scientist-friendly interface for encoding causal models using Qualitative Process (QP) Theory (Forbus, 1984) QCM uses a concept map interface (Novak & Gowin,1984) QCM automatically checks for modeling errors, provides detailed feedback.

11. Bears Disappearing Example

12. Qualitative Concept Maps there probably be a lot more berries because they wouldn’t be eating the berries Number of bears influence the eating rate Bears eat berries Number of berries increase as eating rate decreases

13. Experiment: Automatic classification of the models based on the cultural group they belonged to Data: Interviews collected by Medin’s group Experiment: Detecting culture via models Menominee0 E.A.0 Menominee1 SEQL SEQL Menominee Generalization European American Generalization E.A.1 Menominee2 E.A.2 Menominee3 E.A.3 Example Construct generalizations from concept maps Measure similarity of new model to generalizations about cultures SME SME Example X is more similar to culture Y

14. Results: Culture classification via analogical generalization 81 models encoded using QCM, in response to 3 food web scenarios Results are averaged over 1000 trials. Trial = 4 models from each group chosen randomly for generalization, 8-10 models randomly chosen for test set Conjecture for improving accuracy: Increase uniformity in follow-up questions during interviews. Menomonee Non-Menomonee Overall Accuracy Bears Disappearing 65% 57% 61% Bears Doubling 82% 52% 67% Poplar Disappearing 64% 64% 64% i.e., if a test model for Bears Disappearing was from Menomonee, the system correctly categorized it 65% of the time

15. Can Inspect Generalizations for Insights The number of facts that were consistent across individuals was higher in Menominee models The number of consistent causal relations was higher among Menominee On average, there were 24 facts found consistently across all Menominee models vs. 16 facts for non- Menominee On average, Menominee models contained 4 causal relations whereas non-Menominee models only contained 2.

16. Experiment: Classification via expertise What can we learn from automatic classification of the models based on the level of expertise? Hunters and fishermen are considered experts within this domain Analogical processing results: Classifying experts from non-experts within Menomonee models: 72.5%. Classifying experts from non-experts within European American models: 52% (almost chance) Suggests Menomonee are more influenced by their daily activities than European Americans Consistent with independent manual analysis Menominee hunters more likely than Menominee non-hunters to mention ecological relations (19.8 vs 10.14, p < 0.01) No significant difference between # ecological relations mentioned by hunters versus non-hunters for European-Americans (16.08 vs 16.22, p < 0.97)

17. Next Steps • Possible source of noise: Degree of follow-up questioning varied in interviews • Working with Medin’s group to figure out practical protocols to get more uniform data • Closing the loop: Making predictions from automatically generated models • The same participants could be re-interviewed, or more individuals from same group, depending on level of modeling • Use initial interviews for gathering training set • Construct generalizations, make predictions • Conduct more interviews to test predictions

18. Modeling Moral Decision-Making A S B • Goal: Model effects found in literature on moral dilemmas (e.g., Trolley Problem) • Sacred values vs secular values • Quantity sensitivity • Differences in group responses • Given: a scenario S, outcomes A, B that depend on what action is taken • Predict: which action someone would prefer ??

19. MoralDM model Rules for extracting relevant quantities, producing valuations Utility Calculator Decision New Dilemma SME Vary according to group Cultural or life stories Prior Cases w/Decisions Protected Values

20. Protected Values and Quantity Insensitivity • Protected values (PVs) concern acts and not outcomes • People with PVs show insensitivity to quantity of outcomes (Baron and Spranca 1997, Lim and Baron 1997) • In trade-off situations, they are less sensitive to the consequences of their choices • Quantity insensitivity of PVs are not absolute (Bartels and Medin 2007)

21. Modeling Protected Values 1 child 1 cat $1,000 $1,000,000 • Idea: Use order of magnitude formalism from QR to model protected versus secular values • Introduces stratification in values • Degree of stratification can be varied to model context effects

22. Order of Magnitude • Based on Dague (1988) formalization • A ~K B  |A-B| ≤ K * Max(|A|,|B|) • A and B are equivalent • A ~!K B  |A-B| > K * Max(|A|,|B|) • A and B are comparable – magnitudes tell which is greater • A «K B  |A| < K * |B| • B dominates A, or A negligible w.r.t. B • K determines how stratified values are • K can be adjusted to account for different sensitivities towards consequences • K= 1/10: 20 > 15 • K= 1/3: 20 ~ K 15 • K= 2: 20 >> K 15

23. An Example Dilemma • A convoy of food trucks is on its way to a refugee camp during a famine in Africa. (Airplanes cannot be used.) You find that a second camp has even more refugees. If you tell the convoy to go to the second camp instead of the first, you will save 1000 people from death, but 100 people in the first camp will die as a result. • Would you send the convoy to the second camp? • What is the largest number of deaths in the first camp at which you would send the convoy to the second camp?

24. Scenarios • 12 moral decision making scenarios from Ritov and Baron (1999) were chosen as inputs • civil rights, nature preservers, combating traffic accidents, Jewish settlements, Arab villages,… • Manually translated into predicate calculus • Goal: Semi-automatically translate with EA NLU • Recent Progress: The river scenario was automatically translated from simplified English

25. Simplification Example • Original text: "As a result of a dam on a river, 20 species of fish are threatened with extinction. By opening the dam for a month each year, you can save these species, but 2 species downstream will become extinct because of the changing water level." • Simplified text: "Because of a dam on a river, 20 species of fish will be extinct. You can save them by opening the dam. The opening would cause 2 species of fish to be extinct."

26. Example of EA NLU output "Because of a dam on a river, 20 species of fish will be extinct." (explains-Generic (thereExists dam44262 (thereExists river44314 (and (on-UnderspecifiedSurface dam44262 river44314) (isa river44314 River) (isa dam44262 Dam)))) (thereExists set-of-species44351 (and (isa set-of-species44351 Set-Mathematical) (cardinality set-of-species44351 20) (forAll species44351 (implies (elementOf species44351 set-of-species44351) (and (isa species44351 BiologicalSpecies) (generalizes species44351 Fish) (thereExists extinction44579 (and (isa extinction44579 Extinction) (objectActedOn extinction44579 species44351)))))))))

27. Results • Out of the 12 scenarios, MoralDM makes decisions matching those of participants on 11 scenarios • In 8 scenarios, both first-principles reasoning and analogical reasoning provide the correct answer • In 3 scenarios, first-principles reasoning fails, but analogical reasoning provides the correct answer • In 1 scenario, both reasoning strategies fail

28. Next steps: MoralDM • Test on wider range of examples • Scale up story libraries for different cultural groups • Incorporate MAC /FAC for retrieval • Currently using fables, folktales as sources • Extend EA NLU and QRG-CE coverage to handle range of both cultural stories and interview stories • Story Workbench = EA NLU + MIT’s GUI

29. Future Work • Use automatically constructed cultural models to make novel predictions • Experiment with two-phase interview structure • Continue extending EA NLU to broader coverage • Needed to scale automatic model construction • Extend automatic cultural model construction to other kinds of data • Fables, folk-tales, life stories, valuation rules: i.e., the culturally-specific inputs to MoralDM. • Test MoralDM model on wider range of problems and inputs from different groups • Data is the limiting factor right now

30. Details

31. Elements of QP Theory Cool room Hot brick • Physical Process • All causal changes stem from physical processes. • Example: heat flow between a brick and a room • Parts of physical processes: • Participants • Entities participating in a physical process • Example: the brick, the room • Conditions • Determine when a process is active • Example: difference in temperature • Consequences • Hold as long as a process is active • Direct influences (derivatives) • Indirect influences (functional relations)

32. Modeling Blame Assignment • Context: Computational version of attribution theory from psychology being developed at ICT by Gratch and Mao • Assigns credit/blame for a consequence C of an action A to an agent P based on • Did P cause A? • Did P intend C? • Did P foresee that C would follow from A? • Was P coerced by another actor? • Uses simple axioms to assign binary values of credit/blame to agents based on • causal knowledge, expressed by plans • Simple axioms relating cause, intention, and knowledge • Rules for inferring knowledge and intent from dialogue acts

33. Mao & Gratch’s Computational Model of blame assignment Observed behaviors Plans and Speech Acts Attribution Variables Responsibility Judgment • Based on Shaver’s theory of moral responsibility (1985) • Attribution along dimensions of responsibility • Judgment of responsibility follows • Responsibility may lead to blame • Observed behaviors in a simulation environment • Plan library, using Hierarchical Task Networks, for causal inference • Speech acts covering order negotiation for dialogue inference • Attribution variables as Boolean assignments • Infers which agent in the scenario is to blame

34. QR Model (Tomai & Forbus, 2007) Observed behaviors Plans and Speech Acts Attribution Variables Responsibility Judgment • Same causal/dialogue input, different attribution process • Qualitative representation provides more rigorous modeling method • Social science theories describe dimensions of responsibility are described as continuous parameters • Predictions, experimental results cast as ordinal relationships • Qualitative modeling captures this directly, without ad hoc step of constructing quantitative equations and postulating numerical parameters • Also fits the data better Tomai, E., and Forbus, K. 2007. Plenty of Blame to Go Around:A Qualitative Approach to Attribution of Moral ResponsibilityProceedings of QR-07

35. Who’s to blame? The chairman of Beta Corporation is discussing a new program with the vice president of the corporation. The vice president says, “The new program will help us increase profits, but according to our investigation report, it will also harm the environment.” The chairman answers, “I only want to make as much profit as I can. Start the new program!” The vice president says, “Ok,” and executes the new program. The environment is harmed by the new program. (From Mao 2006, adapted from Knobe 2003)

36. Modes of Judgment Yes Yes • Four distinct modes of judgment • Responsibility is strictly increasing • Translates to six views • Within each mode responsibility is qualitatively proportional to an attribution variable Foreseen? Intended? Coerced? No No No Yes Unforeseen Unintended Coerced Voluntary Increasing responsibility

37. Mao’s Results

38. Survey Results VP 1 3.73 Chair 2 5.63 Chair 4 4.13 VP 3 3.23 Chair 1 3.00 VP 4 5.20 < < < < < VP 2 3.77 Chair 3 5.63

39. QR Model Results VP 4 5.20 Chair 1 3.00 VP 2 3.77 Chair 4 4.13 VP 3 3.23 Chair 2 5.63 < < < < VP 1 3.73 Chair 3 5.63

40. QR Model Results Unforeseen Coerced Voluntary VP 4 5.20 Chair 1 3.00 VP 2 3.77 Chair 4 4.13 VP 3 3.23 Chair 2 5.63 < < < < VP 1 3.73 Chair 3 5.63

41. QR Model Results Unforeseen Coerced Voluntary VP 4 5.20 Chair 1 3.00 VP 2 3.77 Chair 4 4.13 VP 3 3.23 Chair 2 5.63 < < < < VP 1 3.73 Chair 3 5.63 • Violates strict ordering of modes of judgment • Challenges an assumption of Shaver’s theory