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Simulating the ideal eDeliberation

Weiyu Zhang (PhD, UPenn ) Assistant Prof. Communications and New Media National University of Singapore. Simulating the ideal eDeliberation. The Projects. Electronic Dialogue 2000 project (ED2K) and Healthcare Dialogue project (HCD) Random Sample of Americans

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Simulating the ideal eDeliberation

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  1. Weiyu Zhang (PhD, UPenn) Assistant Prof. Communications and New Media National University of Singapore Simulating the ideal eDeliberation

  2. The Projects Electronic Dialogue 2000 project (ED2K) and Healthcare Dialogue project (HCD) Random Sample of Americans Multiple rounds of discussions Debriefing materials and equipments Moderation Full texts of transcribed discussions

  3. Challenges • Unrepresentative sample due to refusals and dropouts • Unequal (potential) influence due to talkativeness • Lack of rationality due to small number of reasons provided

  4. Theoretical Considerations

  5. Simulation Modeling • What if? • Logistic regressions (DV: post-discussion opinions; IVs: amount of talk and number of reasons) to obtain coefficients • Alter the values of each individual data point, re-run the regressions, and save the predicted values • Aggregate the predicted values and compare

  6. Inclusiveness • ED2K • 13 out of 30 >= 5% • Favor more government interventions • Prefer more conservative views on social issues • Significant decreases in Bush’s evaluations and increase in Gore’s • HCD • 3 out of 15 >=5%

  7. Fairness • Only a few changes • 3 out of 30 ED2K measures and 2 out of 15 HCD measures >=5%

  8. Rationality • Almost every variable that was examined shows a change that is equal to or higher than 5% (26 out of 30 in ED2K, 13 out of 15 in HCD). • ED2K • a decreased support in governmental interventions • better evaluations for Bush and lower evaluations for Gore • HCD • contradictory findings compared to the previous two scenarios

  9. Talk vs. Reasons • People who are most talkative do not necessarily have to be the most argumentative. • ED2K r = .57 • HCD r = .88 • The occasional discrepancy suggests that the effect of the amount of talk is often the same as the effect of the number of arguments.

  10. Limitations • Low model fits from .02 to .16 • Operationalization of rationality • The method of simulation modeling

  11. Discussions • 44% of collective opinions that are predicted by simulation models differ from the observed post-discussion opinion distributions at a rate equal to or higher than 5% • treat deliberation findings as only one indicator of deliberate opinions, subject to various errors • Whereas both inclusion and equalization lead to changes in the same direction, maximization of rationality often leads in an opposite direction. • This contradiction implies that normative criteria of deliberation are not empirically consistent.

  12. cnmzw@nus.edu.sg Thank you!

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