Last month’s article, Cognitive Bias and the Law, covered the effects of cognitive bias on legal decision making under risk.1 In legal decision making, lawyers regularly rely on conventional wisdom. They use instinct and intuition to make decisions, but conventional wisdoms are based on an extremely small set of experiences. The most seasoned lawyers may have what seems like a large body of experience, but in reality it is too small a sample upon which to rely.2 This type of thinking is what behaviorists label as cognitive bias. Reliance on answers that feel right are based on heuristics, or System 1 thinking, which is an innate and instinctive response to inputs of information that require a decision. This instinctive response is made quickly and without thought. This method may be appropriate for simple decisions, but decisions involving more complex information require a more systematic thought process.

The existence of the cognitive biases, as described in the Prospect Theory, means that reliance on conventional wisdom is not the optimal way for lawyers to make decisions under risk. The Prospect Theory3 provides that in making decisions under risk, parties weigh the relative merits of each specific decision on a case by case basis. This method contrasts with the rational actor, or expected utility theory, that all individual decisions are made based on the improvement of the actor’s total state of wealth.4 Under the Prospect Theory, decisions are made in relation to a reference point. When presented with decisions under risk, if the decision involves preserving a gain, the actor is risk adverse.5 Conversely, if the decision will result in a loss, the actor is risk-taking.6

Legal decision-making is constantly subjected to these opposite states of thoughts and the difficulties they cause. In a transaction or a legal dispute, one side is seeking a gain. One side is seeking to avoid a loss. Given these states, each party sets the “frame” differently and becomes anchored on the outcome choice that satisfies their frame.7 In addition, parties have an inflated value of things they possess, as opposed to those they wish to acquire.8

This “endowment effect” makes rational transactions difficult to complete. Adding to the impasse, as a legal matter continues, the parties’ reference points change. A seller, or a plaintiff, may begin to view the outcome as avoiding a loss, and the buyer or a defendant may begin to view the outcome as a gain. Even more difficult, is the litigation or transaction in which both parties’ frame is a loss. The contentious and bitter nature of divorce litigation is an example. Rather than seeing the equitable distribution of property as a gain, if both parties perceive it as a loss, the case becomes intractable. Understanding each party’s reference points and how they may change is critical to a successful outcome.

A look at some common conventional wisdoms illustrates the strong influence of cognitive bias on legal decision making.

Conventional Wisdom: “Don’t Negotiate Aggressively; Don’t Blow the Deal”

Every lawyer has heard the admonition from a client during a contract negotiation not to “blow the deal” through hard bargaining. Clients view lawyers as a necessary evil, but they do not trust them to get the deal done. Implicit in this concern is the client’s belief that lawyers increase fees through lengthy and over complicated documents, which extends the time period for negotiation and jeopardizes reaching agreement. The client often insists that that the lawyer draft a short and simple agreement or, to reduce legal fees, the client insists that the other party’s lawyer prepare the first draft of the agreement.

The conventional wisdom that lawyers who aggressively negotiate a deal risk a failed negotiation is wrong. It is the first draft of the agreement and the economic terms offered in that draft, which set the final price and terms of the deal.9 An aggressive offer sets the frame, and a more aggressive offer presents a more advantageous frame for the offeror. Once the terms and a purchase price are presented, the offeree anchors on the terms from the receiving party and will not deviate significantly from them.10 Moreover, once the anchor is set, confirmation bias causes the party to seek out information to support the anchor.11 In addition to anchoring, the status quo effect and risk aversion cause the parties to be reluctant to change the existing state of affairs–the proposed contract is treated as the baseline and is negotiated rather than discarded and a new contract prepared.12

At the same time, opposing counsel is receiving the same instruction from the client–“Don’t blow the deal.” Desiring to meet the client’s expectations, the lawyer is unlikely to negotiate aggressively. Indeed, the lawyer is prohibited from doing so under ethics rules, as the lawyer must follow the client’s instructions, even if the lawyer thinks the course of action will be harmful to the client.13 In addition to aggressive terms, presenting a lengthy and complex agreement makes it difficult for opposing counsel to process the information and make an aggressive counter offer–cognitive bias causes the lawyer to default to System 1 thinking.14, 15 Finally, by including extensive and complicated terms in an agreement, the drafting party can then concede terms that are unimportant. This is because the cognitive bias of reciprocation makes people desire to return a favor given–opposing counsel will be inclined to reciprocate and concede terms that are materially important.16

In contract negotiations the best strategy is to make the most extreme opening offer and take advantage of the opposing party’s cognitive biases to obtain a favorable deal.17 Failure to follow this strategy is very costly. Each one dollar increase in an opening offer results in close to a fifty-cent increase in the final price.18

Conventional Wisdom: “All Cases Settle. This Case Will Never Go To Trial.”

Lawyers often advise clients that 99 percent of cases settle. Implicit in this statement is the idea that pursuing litigation is a sound strategy to force resolution of a dispute–the bludgeon of litigation will bring the parties to the negotiating table and the case will never reach a judge or jury. This is a faulty assumption. As with contract negotiations, even the most experienced lawyer has not handled enough to cases to assess the probability of settlement based on her own experiences– once again the Rule of Small Numbers rears its ugly head.

A review of the actual data shows the 99 percent settlement figure is wrong.19 Although it is true that only about only 1 precent of cases go to trial, this does not mean that the remainder settle or that they settle on favorable terms. Statistics from the United States federal court system show that in the federal district courts, 20 percent of cases are terminated by one of the parties, while 79 percent are terminated by court action.20 This analysis does not equate to a 99 percent settlement rate, because the coding for these dispositions are not indicative of a successful settlement. A disposition by a judge, or the parties, ranges from a default judgment to a final order approving a settlement by the parties.21

The real settlement rate is 65-70 percent.22 This statistic means that 30-35 percent of filed cases result in an unfavorable outcome. Additionally, using a broad percentage does not consider that the settlement rate varies widely by case type. Employment discrimination cases have a settlement rate of 55 percent while contract cases have a settlement rate of 70-75 percent.

As in contract negotiations, settlement negotiations are subject to cognitive biases. The cognitive biases of plaintiffs, defendants, and their lawyers are easily established and harden quickly, even with test subjects who have little stake in the outcome.23 As one study noted:

A study of law students who were randomly assigned to act as either plaintiff or defendant counsel quickly adapted the position of the party they were assigned to advise, given the same set of facts to each side. The law students did not engage in any nuanced analysis of the facts presented to the. Rather, they immediately found a way to justify the position that best suited their fictional client.24

The lawyer representing the client in settlement negotiations is also subject to cognitive bias. Lawyers are overconfident in their assessment of cases, and this overconfidence is not warranted.25 While being overconfident, lawyers are also risk averse like anyone else.26

These opposite mindsets cause irrational risk aversion and risktaking, as well as failure to reach settlement, even when it is the optimal outcome. Studies of actual cases where settlements were attempted but failed, and were subsequently adjudicated show that both plaintiffs and defendants have a high error rate.27 That is, the settlement which was rejected would have been a better result than the litigated outcome. The plaintiff’s error rate was 61 percent, at an average loss of $44,000.28 The defendant’s error rate, while only 24 percent, resulted in an average loss of $1,140,000, showing the risk-taking nature of a party facing a perceived loss.29 An error rate of 61 percent on an overall settlement rate of 70 percent means that 40 percent of cases that should be settled are not, resulting in a worse outcome. This is almost no better than the 50/50 odds of a coin toss.

The influence of cognitive bias in litigation and settlement negotiations makes it extremely difficult to reach a settlement. Without understanding the impact of cognitive bias, a lawyer cannot make a meaningful assessment of whether to settle or litigate a case.

Conventional Wisdom: “We Have a Favorable Judge, Arbitrator, or Jury.”

Lawyers are trained in the case method. In law school and beyond, lawyers are taught that judges decide cases by applying the facts to prior legal precedent.30 The reality is that judges decide cases based on their own personal views and the influence of cognitive bias. Political ideology and other personal preferences are predictive of Supreme Court case outcomes.31 The same is true of United States Circuit judges. 32

Judges are also subject to cognitive biases, which leads to faulty decision making, and decisions which are inconsistent. The cognitive bias of anchoring is reflected in damage amounts.33 Judges will award higher damage amounts based solely on the amounts mentioned, suggested, or offered by one of the parties in various parts of the proceeding. The amount of damages that anchor the judgment amount can range to those raised at motion to dismiss hearings, in settlement conferences, and in pretrial conferences.34 In studies conducted to assess anchoring in judges, judges in the control group where damages were not discussed or were discussed generally with no specific damage amount, awarded much lower damages.35

Judges also make significant errors by relying on intuition instead of relying on empirical data and basic science. For example, although the likelihood of liability based on the mathematical probabilities of an event being caused by one of the parties is small, a judge will rely on the higher number framed within the problem, ignoring basic math. When a 90 percent probability is mentioned, but other events that occurred reduce the 90 percent probability, the judge ignores the mitigating factor. In one study, the actual probability was slightly more than 8 percent, but 75 percent of the judges found liability because one of the facts mentioned a 90 percent chance of an event occurring and ignored the other facts which reduced this probability to less than 8 percent.36

Knowing who a judge is, and the history of her decision-making in specific cases, is just as important as understanding the effect of cognitive biases. Consider that judges who are graduates of Louisiana State University impose harsher sentences on defendants in the week following an unexpected loss by the LSU football team.37 These sentences also fall disproportionately on minority defendants, regardless of the race of the judge.38 Another finding is judges will avoid making hard decisions before a meal, and defer those make decisions until after a meal.39 Additionally, a judge will consider prior judgments in assessing the next judgment–if three consecutive defendants are found guilty, the judge is prone to the Gambler’s Fallacy and finds the next defendant innocent.40 Judges subject to re-election are more likely to rule for plaintiffs.41

Surprisingly, given the large number of disputes which are resolved through arbitration, there has been little research on the effect of cognitive bias on arbitrators. The available research shows that arbitrators are no better than judges at resisting cognitive bias in decision making.42 One simple test of the influence of cognitive bias is the Cognitive Reflection Test (“CRT”). It asks three simple questions, each with an intuitive incorrect answer:

1. A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?
2. If it takes five machines five minutes to make five widgets, how long would it take 100 machines to make 100 widgets?
3. In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes forty-eight days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake?

The intuitive answers are: ten cents, one hundred minutes, and twenty-four days. These answers are wrong.43

Arbitrators taking the CRT score similarly to judges, with a mean score of 1.47 correct answers–no better than half.44 And the decision making of arbitrators reflects this. Arbitrators are subject to framing, anchoring, and other cognitive biases.45

Juries are no better than arbitrators and judges. Asking for a higher damage award, even where not justified, increases a jury’s award.46 Statutory damage caps intended to limit damages, particularly punitive damages, have the effect of increasing damage awards. The higher the cap, the higher the punitive damage award.47

These examples and others cognitive biases mean that taking a case to trial is a risky proposition, regardless of the forum and the decision maker.

In conclusion, cognitive biases are deeply imbedded in human nature. Convincing a client of the influence of cognitive bias and the need to re-examine her decision-making is extremely difficult. Even when people are advised of the true facts, their behavior is unchanged. This occurs because people are overconfident. Consider three simple examples. When presented with evidence that people are generally poor drivers and have a high rate of accidents, respondents will insist they are different–they are superior drivers.48 When couples are separately asked what percentage of the household duties they perform, the total percentage is well over a 100 percent–each partner has an overinflated view of his contributions.49 And, finally, when advised that 50 percent of all marriages end in divorce, almost all respondents who are asked about the likelihood of their marriage ending in divorce insist they are sure they will not get divorced.50

Overcoming cognitive bias would require it to be rectified in numerous actors–the clients, the lawyers, and in the instances where a client is a corporate entity, the employees advocating for the corporation. This is a tall order and unlikely to be achieved. Moreover, there is no consensus on successful ways to combat cognitive bias. The most prominent techniques are considering the opposite, group decision-making, and allowing time and reflection before making decisions under risk. Some studies suggest that these overcome the biases, while others find the techniques ineffective.51

One way to combat these biases is to seek out and rely upon empirical data when making decisions under risk. The availability of new technologies provides this data and the means to analyze it in way that the human brain cannot. Next month’s article will focus on these technologies and the promise they hold for better legal decision making under risk.

1. Available at https://www.dealercounsel.com/defender/defenderseptember-2019.
2. This is the Rule of Small Numbers (which is a fallacy, not a rule, and should not be followed), which states that a small sampling of events does not accurately predict the outcome of future similar events. Amos Tversky & Daniel Kahneman, Belief In The Law Of Small Numbers, Psychological Bulletin Vol 76, (February 1971), at 105-10.
3. The Prospect Theory was described more fully in the first article and is described briefly herein.
4. Amos Tversky & Daniel Kahneman, Prospect Theory: An Analysis of Decision Under Risk, Econometrica, (Mar. 1979), Vol. 47, No. 2, at 263- 92. See also Daniel Kahneman, A Perspective on Judgment and Choice, Mapping Bounded Rationality, American Psychologist (Sept. 2003).
5. Tversky, supra, at 279-80.
6. Id.
7. Anchoring is the reason cars are advertised at $29,995 rather than $30,000. The car buyer anchors on the first number of the series, the “2”, and thinks of the price is in the lower $20,000 range, and not in the $30,000 range. Marco Bertini & Luc Wathieu, The Framing Effect of Price Format, Working Paper 06-055 (May 16, 2006).
8. Daniel Kahneman, et al., Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias, The Journal of Economic Perspectives, (Winter 1991), Vol. 5(1), at 193-206.
9. Dan Orr & Chris Guthrie, Anchoring, Information, Expertise and Negotiation; New Insights From Meta-Analysis, 21 Ohio St. J. on Disp. Resol., 597, 609 (2006); Fritz Strack & Thomas Mussweiler, Heuristic Strategies for Estimation Under Uncertainty: The Enigmatic Case of Anchoring, Foundations of Social Cognition 79 (“[T]he number that starts the generation of a judgment exerts a stronger impact than do subsequent pieces of numeric information.”).
10. Scott Plous, The Psychology of Judgment of Judgment and Decision Making at 151 (1993). (“People adjust insufficiently from anchor values, regardless of whether the judgment concerns the chances of nuclear ware, the value of a house, or any number of other topics”); Dan Orr & Chris Guthrie, Anchoring, Information and Expertise and Negotiation, New Insights From a Meta-Analysis, 21 Ohio St. J. on Disp. Resol. 597, 625 (2006) (Negotiators can benefit from anchoring by proposing more self-serving positions that cannot be reasonably justified, particularly with less experienced negotiators and where offeree has little information to evaluate value of offer.).
11. Confirmation Bias is the tendency to seek out and accept only information which supports the actor’s existing view. Raymond S. Nickerson, Confirmation Bias: A Ubiquitous Phenomenon in Many Guises, Review of General Psychology, (June1988) Vol. 2, No. 2, at 175-220.
12. Robert L. Wharf & Francesco Parisi, The Role of Status Quo Bias and Bayesian Learning In the Creation of new Legal Rights, 3 J. L. Econ & Pol’y 25, 26 (2006).
13. ABA Model Rules of Conduct 1.2, Scope of Representation and Allocation of Authority Between Client & Lawyer (The lawyer shall abide by client’s decisions concerning objectives of representation.).
14. Russell Korobkin, The Efficiency of Managed Care “Patient Protection” Laws, Incomplete Contracts, Bounded Rationality and Market Failure, 85 Cornell L. Rev. 1 (1999) (inability to absorb large and complicated amounts of information prevents ability to make rational decisions); Cass R. Sunstein, Predictably Incoherent Judgments, 54 Stanford L. Rev., 153, 1163 (2002) (inability to process large amounts of information prevents informed and reasoned decision making).
15. Russell Korobkin, The Borat Problem in Negotiation: Fraud, Assent, and the Behavioral Law and Economics of Standard Form Contracts, 101 Cal. L. Rev. 51, 78 (2013) (“When drafters want to discourage reading, they can increase the costs of doing so by increasing the length and opacity of their standard forms.”); Jeff Sovern, Toward a New Model of Consumer Protection: The Problem of Inflated Transaction Costs, 47 Wm. & Mary L. Rev. 1635, 1660-61 (2006) (criticizing the inflation of transaction costs by sellers, such as by increasing contract complexity, to deter buyer detection of unfavorable terms).
16. Robert V. Calvinism, Influence: The Psychology Of Persuasion, 17 (1st Ed. 1993).
17. This tactic implicates the question of a lawyer’s obligation of truth and candor to opposing counsel, the court, and third parties. See, e.g., Rule 3.1, Meritorious Claims & Contentions, American Bar Association, Center for Professional Responsibility (2016) (A lawyer shall not assert an issue in a proceeding unless basis in law and fact.).
18. Dan Orr & Chris Guthrie, Anchoring, Information, Expertise and Negotiation, supra, footnote 10. See also, Opening Offers and Out-of-Court Settlement: A Little Moderation May Not Go a Long Way, 10 Ohio St. J. Disp. Resol. 1 (1994) (Extreme opening offers are more likely to result in higher settlements than more moderate opening offers.).
19. Theodore Eisenberg & Charlotte Lanvers, What Is the Settlement Rate and Why Should We Care?, Cornell Law Faculty Publications, Paper 203 (March 2009); Marc Galanter & Mia Cahill, “Most Cases Settle”: Judicial Promotion and Regulation of Settlements, 46 Stanford L. Rev. 1339, 1339-40 (1994) (Common estimates of settlement rates of 85-90 percent are inaccurate.).
20. U.S. District Courts-Civil Cases Terminated by Action Taken, During the 12 Month Periods Ending June 30, 1990, and September 30, 1995 Through 2018, https://www.uscourts.gov/statistics-reports/caseload- statistics-data-tables, (last visited October 15, 2019).
21. Eisenberg, supra, at 117.
22. Andrew J. Wistrich & Jeffrey J. Rachkinski, How Lawyers’ Intuitions Prolong Litigation, Cornell Law Faculty Publications, Paper 602 (March 1, 2013).
23. See, e.g., Holger Spamann, Lawyers’ Role-Induced Bias Arises Fast and Persists Despite Intervention, John M. Olin Center For Law, Economics, and Business, Discussion Paper No. 1005 (May 2019).
24. Russell B. Korobkin & Thomas S. Ulen, Law and Behavioral Science: Removing the Rationality Assumption from Law and Economics, 88 Cal.L.Rev. 1051(July 2000).
25. Jane Goodman-Delahunty, et. al., Insightful or Wishful: Lawyers’ Ability to Predict Case Outcomes, Psychology, Public Policy, and Law, Vol. 16, No. 2, at 133-157 (2010).
26. Claudia M. Lando, et. al., Playing Against an Apparent Opponent: Liability and Litigation Under Self Serving Bias, unpublished (April 15, 2011). Available at http://dev.gtcenter.org/Archive/2011/Conf/Nikitin1324. pdf.
27. Randall L. Kiser, et al., Let’s Not Make a Deal: An Empirical Study of Decision Making in Unsuccessful Settlement Negotiations, Journal of Empirical Legal Studies, Volume 5, Issue 3, 551–591 (Sept. 2008) (review of actual case outcomes to rejected settlement offers shows decision not to settle was erroneous and harmful). This study confirmed the work of three prior studies. See Samuel Gross & Kent Syverud, Getting to No: A Study of Settlement Negotiations and the Selection of Cases for Trial, 90 Michigan L. Rev. 319 (1991); Samuel Gross & Kent Syverud, Don’t Try: Civil Jury Verdicts in a System Geared To Settlement, 44 UCLA L. Rev. 51 (1996); Jeffrey Rachlinski, Gains, Losses and the Psychology of Litigation, 70 S. Cal. L. Rev. 113 (1996). These studies are particularly revealing as the actual results were compared to the proposed settlements by collecting actual case outcomes and interviewing the attorneys involved in the litigation. Studies based on statistical probabilities show nothing about cause and effect. Probability does not address whether inclement weather causes the barometer to fall or the fall in the barometer causes inclement weather. Probability is merely the likelihood that the result, based on an event (e.g., a lawyer’s decision) will fall close to the expected outcome, and will not deviate significantly from the expected outcome. An excellent and accessible explanation of probability is Against All Odds, The Remarkable Story of Risk, Peter L. Bernstein (1996).
28. Kiser, Let’s Not Make a Deal, supra.
29. Id.
30. This case method was the basis of law school teaching beginning in the early nineteenth century, based on the philosophy of Christopher C. Langdell who thought that law could become a precise science. Using a small number of cases, the outcome of future cases with the same legal principles could be predicted. Edward L. Rubin, What’s Wrong with Langdell’s Method and What to Do about It, 60 Vand. L. Rev. 609 (2007).
31. Jeffrey J. Rachlinkski, & Andrew J. Wistrich, Judging the Judiciary by the Numbers: Empirical Research on Judges, 13 Annual Review of Law and Social Science (2017)(“An avalanche of research demonstrate that US Supreme Court justices make decisions that align with their political attitudes.”); Jeffrey Segal & Harold Spaeth, The Supreme Court and the Attitudinal Model Revisited, (2015) (Ideology and not rational choice or legal precedent determine supreme court decision-making.); Aaron Belkin, et al., Chief Justice John Roberts is Not a Moderate, Take Back the Court (October 2019) (Statistical analysis shows alignment of most conservative judges in 75 percent of close decisions, while more liberal judges were only aligned in 8 percent of cases, which is aberration of expected outcome), https://static1.squarespace.com/ static/5ce33e8da6bbec0001ea9543/t/5d98d1c2a141d425ee063b570296259377/Chief+Justice+Roberts+Is+Not+A+Moderate.pdf.
32. Rachlinski, Judging the Judiciary, supra.
33. Greg Pokarsky & Linda Babcock, Damage Caps, Motivated Anchoring, and Bargaining Impasse, Journal of Legal Studies, (Jan. 2001) Vol. XXX, at 143-59.
34. Chris Guthrie, et al., Blinking on the Bench: How Judges Decide Cases, Cornell Law Faculty Publications, Paper 917 (2007) at 19.
35. Id.
36. Id. at 23.
37. Ozkan Eren & Naci Mocan, Emotional Judges and Unlucky Juveniles, American Economic Journal: Applied Economics, (Sept. 2018), Vol. 10(3), at 171–205.
38. Id. at 191-92. There is a great deal of evidence of racial bias in judicial decision-making, especially in criminal sentencing. A meta-analysis of seventy-one studies of sentencing found that African American defendants receive much harsher sentences than white defendants in an overwhelming number of cases. Ojmarrh Mitchell, A Meta-Analysis Of Race And Sentencing Research: Explaining Inconsistencies, J. Quant. Criminol. 21(4):439-66 (2005).
39. S. Danziger et al., Extraneous Factors In Judicial Decisions, Proceedings of the National Academy of Sciences of the United States of America (April 2011). Vol. 108 (17), at 6889-992.
40. Daniel Chen, et. al., Decision-Making Under the Gambler’s Fallacy: Evidence from Asylum Judges, Loan Officers, and Baseball Umpires, National Bureau of Economic Research Working Paper 22026 (2016) (“Judges are up to 3.3 percentage points more likely to reject the current case if they approved the previous case. This translates into two percent of decisions being reversed purely due to the sequencing of past decisions, all else equal.”).
41. Alexander Tabarrok & Eric Helland, Court Politics: The Political Economy of Tort Awards, 42 J.L. & Econ. 157, 186-87 (1999) (“Elected judges know they rule at the discretion of the voters, and, like other politicians, they rule accordingly.”).
42. Susan D. Frank, et. al., Inside the Arbitrator’s Mind, 66 Emory Law Journal 1115 (2017).
43. The correct answers are: 1) $.05 cents, with the bat costing $1.05 and the ball costing $.05 cents; 2) five minutes, as the output per machine is the same; and 3) forty-seven days, as the lake doubling each day means that if the lake is covered on day forty-eight, only one additional day after day forty-seven will result in lake being fully covered.
44. The highest median score belongs to MIT students, whose mean score was 2.18. Shane Frederick, Cognitive Reflection and Decision Making, 19 J. Econ. Persp. 25, 29 (2005). A study involving almost all the circuit court trial judges in Florida found that the median score for the judges who took the CRT was 1.23. Chris Guthrie, et al., Blinking on the Bench: How Judges Decide Cases, Cornell Law Faculty Publications, Paper 917 (2007), at 15.
45. Frank, Inside the Arbitrator’s Mind, supra.
46. However, the conventional wisdom that arbitrators split the baby in decision-making is not accurate. Gretchen B. Chapman & Brian H. Bomstein, The More You Ask For, the More You Get: Anchoring in Personal Injury Verdicts, 10 Applied Cognitive Psychol. 519, 525-28, 532-33 (1996).
47. Jennifer K. Robbennolt & Christina A. Studebaker, Anchoring in the Courtroom: The Effects of Caps on Punitive Damages, 23 Law & Hum. Behav. 353, 357 (1999).
48. Ola Svenson, Are We All Less Risky and More Skillful Than Our Fellow Drivers?, 47 Acta Psychologica 143, 145-46 (1981) (claiming a “strong tendency to believe oneself as safer and more skillful than the average driver”).
49. Michael Ross & Fiore Sicoly, Egocentric Biases in Availability and Attribution, 37 J. Personality & Soc. Psychol. 322 (1979) (reporting experimental evidence that people more readily recalled their own contributions to group projects and accepted more responsibility for group success than others attributed to them).
50. Lynn A. Baker & Robert E. Emery, When Every Relationship Is Above Average: Perceptions and Expectations of Divorce at the Time of Marriage, 17 Law & Hum. Behav. 439, 441-43 (1993) (detailing the results of a study measuring marriage license applicants’ perceptions of the frequency of divorce in the U.S.; while respondents predicted a 50 percent divorce rate, the median probability given for their own marriages ending in divorce was zero).
51. Compare Linda Babcock, et al., Creating Convergence: DeBiasing Biased Litigants, 22 Law & Soc. Inquiry 913 (1997) (asking litigants to consider weakness in their case increases settlement rate) with Jane Goodman- Delahunty et al., Insightful or Wishful: Lawyers’ Ability to Predict Case Outcomes, Psychology, Public Policy, and Law, Vol. 16, No. 2, 133-157 (2010) (asking lawyers to generate list of reasons they might be wrong did not reduce cognitive bias of unwarranted optimism in outcome).

Oren Tasini is a Partner with Killgore Pearlman. He was one of the Founding Members of the National Association of Dealer Counsel.