Scientific Proof of Law’s Overreliance On Reason: The “Reasonable Man” is Dead and the Holistic Lawyer is Born

July 17, 2016

brain_gears_NOLast month I wrote about the place of reason in the law. The Law’s “Reasonable Man,” Judge Haight, Love, Truth, Justice, “Go Fish” and Why the Legal Profession Is Not Doomed to be Replaced by Robots. That article discussed how reasonability is the basis of the law, but that it is not objective. It depends on many subjective factors, on psychology. This article elaborates on this key point. The Law’s Reasonable Man is a fiction. He or she does not exist. Never has, never will. All humans, including us lawyers, are much more complex than that. We need to recognize this. We need to replace the Law’s reliance on reason alone with a more realistic multidimensional holistic approach.

Although logic and reasoning are important, we have many other important capacities, including empathy, intuition and imagination. All of these capacities are required for the practice of law. That is why lawyers cannot be replaced by robots. The fact the Reasonable Man is a fiction is something we lawyers should celebrate, not sweep under the carpet.

Most human decisions are not even based on reason. Quaint notions to the contrary are derived from the 18th Century Age of Reason. They are completely out of touch with reality. They are contrary to what science today is telling us about how humans process information and reach decisions.

frozenbrainsScientific research shows that the cornerstone of the Law – Reasonability – is not solid granite many had thought. There are no hard gears in our head, just soft, gelatinous, pinkish-beige matter. (Our brain is only soft grey matter when dead.) The ratiocination abilities of the brain are just one small part of its many incredible capacities. (For example, MIT scientists have shown that we can identify images seen for as little as 13 milliseconds, 13/1,000ths of one second.) We are far more than just rational, and that is a good thing.

Going Beyond the Age of Enlightenment Into the Modern Era of Science

Only_Humans_Need_ApplyThis article will offer proof that the Law’s Reasonable Man is dead. This is a cause for optimism because, as noted, if we were just reason-based workers, then our functions would soon be automated. We would soon all be out of work. See eg. Thomas H. Davenport, Julia Kirby, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (Harper 2016). Although most lawyers in the profession do not know it yet, the non-reasoning aspects of the Law are its most important parts. The reasoning aspects of legal work can be augmented. That is certain. So will other aspects, like reading comprehension. But the other aspects of our work, the aspects that require more than mere reason, are what makes the Law a human profession. These job functions will survive the surge of AI.

If you want to remain a winner in future Law, grow these aspects. Only losers will hold fast to reason. Letting go of the grip of the Reasonable Man, by which many lawyers are now strangled, will make you a better lawyer and, at the same time, improve your job security.

In today’s AI driven economy we all need to change our work to include more of our human capacities than mere reason. This is transforming all work, not just legal. We are far more than a thinking machine. We must open our eyes and see the truth. That is the true meaning and ultimate conclusion of the Age of Enlightenment.

Schrodinger's CatScience based on reason and the experimental method has taken Man beyond the rational, has shown the limitations of reason. Just as the evidence from physics experiments forced scientists to go beyond Newtonian Causality, and required them to embrace the seemingly irrational truth of Relativity and Quantum Mechanics, so too must the Law now evolve its thinking and procedures. As proof for this proposition in this chapter I will proffer the testimony of one expert witness, a noted MIT and Duke University Psychologist and Behavioral Economist.

The Legal Profession Must Awaken from the Daydream of Rationality

Reasonable_guageMy last blog, The Law’s “Reasonable Man”, laid the foundation for the introduction of this evidence. I noted how the law is based on the assumption that people make reasoned decisions and are capable of acting in a reasonable manner. I offered preliminary evidence that this assumption is contrary to the findings of research psychologists. I referred to a recent article by one such psychologist, Herb Roitblat: The Schlemiel and the Schlimazel and the Psychology of Reasonableness (Jan. 10, 2014, LTN) (link is to republication by a vendor without attribution).

I will now offer further, more detailed proof that humans do not act solely out of reason. Some just delude themselves into thinking so. I will then argue that these findings require us to make fundamental reforms to our system of justice. These reforms will both improve the our justice system and ensure the survival of the legal profession. Lawyers will remain, but they will look and work much differently than they do today. They will be augmented by AI, but not automated and replaced by AI. Productivity and efficiency will go through the roof. Our system of justice will vastly improve. To get there the profession will first have to awaken from the daydream of rationality. This article is designed as a wake up call.

This evidence of logic’s limitations is abundant. With only a little search I am sure you will find much more proof than I will now proffer. Many of you already know this from long experience in the courtrooms and law offices of the world, but may now have heard of the scientific proof. The evidence proves that the old assumptions on human reasonability, assumptions built centuries ago when the Age of Reason first began, are false. The evidence shows that the Reasonable Man is a legal fiction.

As Exhibit “A” to the assumption busting proposition I rely on the work of Dan Ariely, a Professor of Psychology and Behavioral Economics at Duke University. As an introduction to his work I ask readers to stop and take a few minutes, right now, to watch the TED video by Professor Ariely, Are We In Control of Our Own Decisions? He refers to his many scientific experiments at MIT and Duke that show we are not in control of many of our own decisions, even seemingly simple ones. These experiments prove my point.

Predictable Irrationality and Swearing on Bibles

Need more proof? Then please consider additional testimony from Professor Ariely on predictable irrationality. It is on another video called We’re All Predictably Irrational. This discourse even mentions every e-discovery lawyer’s favorite company, Enron, and examines our basic moral code, our personal fudge factor. Dan has conducted many experiments on the all too human tendency to cheat and lie, if only just a little, and the moving grey line between acceptable and unacceptable behavior. This is the line that the Law is constantly asked to draw, and to evaluate. These psychological insights are important to all lawyers, especially discovery lawyers, of the “e” only type like me, or not. Again, please listen carefully and consider the implications of these findings on the Law.

One interesting finding from Professor Ariely’s scientific experiments on cheating, one that you can easily miss in the predictable irrationality video, is that asking people to swear on a Bible significantly reduces cheating. This even works for atheists! I kid you not. Perhaps we should bring back the old tradition of requiring all witnesses to swear on a bible before beginning their testimony?

Ralph_swearing_oath_bibleI have done this myself long ago when I was out taking depositions as a young lawyer. In the early eighties many court reporters in rural counties of Florida would still pull out a Bible before a deposition began (they all used to carry them around for that purpose, and yes, that was way before they started carrying around computers). The court reporter would then ask the deponent to raise their right hand and put their left hand on the Bible. All the witnesses I saw instantly complied, thinking erroneously that this was a legal requirement. They placed their hand on the Bible, some nervously, and some like they did that all the time. Then they were asked to solemnly swear on the Bible that they would tell the truth, the whole truth and nothing but the truth so help me God. They did as asked by the serious court reporter, and some seemed pretty impressed by the whole ceremony. I recall that overall the testimony from these witnesses was pretty good, meaning less lies than usual.

I only saw this done a few times, and, as a typical arrogant big city lawyer (yes, out in the rural areas where they were still doing this, they all thought of Orlando as a big city), I dismissed it as a quaint old custom. But now science shows that it works.

What are the implications of these findings about human behavior? Maybe we should bring back Bibles into the courtrooms? Or at least bring back a bunch of solemn oaths? If we do not require swearing on or to a Bible, due to Church and State, or whatever, then perhaps we should ask people giving testimony to swear on something else. Most anything seems to work, even if it does not really exist. Dan Ariely’s experiments found that it even worked to have MIT students swear on an honor code that didn’t exist. Maybe asking lawyers to swear on their ethics codes would work too? Maybe that is the next reform in the procedural rules we should push for. Maybe we should update Rule 603 of the Federal Rules of Evidence:

Before testifying, a witness must give an oath or affirmation to testify truthfully. It must be in a form designed to impress that duty on the witness’s conscience.

prisoner_ralph_chainsWe need to work on forms designed to impress today’s savvy witnesses. Maybe bringing back Bibles will work for some, or something custom-fit to the particular witnesses. Who knows, for a chemist, it might be the periodic table. For others it might be a picture of their mother. Maybe the oath should be administered by prisoners in chains and mention the penalties of imprisonment for perjury. I think that would be pretty effective. Have you ever seen prisoners in chains up close in the courtroom? A few judges I know used to handcuff and shackle fathers who were delinquent in child support payments before their hearings. I am told it had a very sobering effect. Some experiments with this should be conducted because our current systems are not working very well. We rarely impress witnesses enough to awaken their latent conscience, much less their lawyers.

Maybe we should also amend Rule 26(g) to add swearing and a reference to ethics codes? Maybe stronger, more impressive oaths by lawyers signing 26(g) discovery requests and responses would work. Perhaps that would magically make more all too human lawyers start taking the requirements of the rules more seriously.

Lord Phillips 2009Maybe we should follow the British and make our judges wear fancier robes and make our lawyers and judges wear wigs? (One of Ariely’s experiments found clothing had an impact on honesty.) Let us build even more impressive courtrooms while we are at it, and let’s not only say Your Honor, but how about Your Lordship too? Or Your Grace? Maybe all lawyers should start adding courtly formalities to their 26(f) conferences? I can just imagine defense attorneys beginning every one of their responsive statements with things like: “The right honorable attorney representing the plaintiffs in this proceeding has made a point with some validity, but …” Maybe that would motivate lawyer conduct that would in fact please the court?

Of course I jest, but Ariely’s work shows that irrational approaches have a better chance of success than appeals to abstract knowledge alone. Forget about using reason to appeal to lawyers to cooperate, we have all seen how far that gets us.

Doing the Right Things for the Wrong Reasons

Are you a die-hard rationalist and demand more proof that the Reasonable Man is a myth? More evidence? Then listen to Dan Ariely’s Doing The Right Things for The Wrong Reasons.

Professor Ariely talks about more of his experiments. They show how immediate, tangible, emotions and concrete facts are a much more powerful motivator than all abstract knowledge. This means that one sanctions case invoking fear will do much more to encourage cooperation than a thousand law review articles. In my experience judges that threaten harsh punishment, that are known not to tolerate discovery misconduct, tend to have fewer disputes. Now we know why. Fear is a more powerful motivator than reason. As he shows in the video, for some people a good glass of wine is a powerful motivator too.

Dan_Ariely_toastProfessor Ariely’s testimony in this video examines the big gap between everyone’s knowledge of what they should be doing, and what they actually are doing. The truth is, we often do not act reasonably. There are many other more powerful forces at work. One of the most important is environment, and thus my earlier comments on impressive courtrooms, wigs, courtly conduct, and the like. Brand names and price have the same kind of impact. Many clients are still impressed by the big-firm, fancy reception room syndrome. People tend to think about fine wine and lawyers in the same way.

In the second half of this testimony Dan Ariely started to share some of the solutions he has come up with to these problems, ways to trick yourself and others into doing the right thing. One such motivator is public recognition, pride. Remember his discussion regard Prius owners. So how about Cooperation awards of lawyers? Proportionality awards for judges, etc. Let’s award a whole lot of gold, silver and especially bronze medals. I am serious about this awards and recognition proposal. If you have any interest in funding such awards, or otherwise being involved, please let me know. This would be a good opportunity for vendors in the legal space, especially e-discovery vendors.

Mere intellectual appeals to change behavior are almost useless. You have to persuade the whole human, and that requires addressing emotions and many other subconscious factors. That requires far more than abstract, knowledge-based writings.

The Power of Emotions and the Myth of Reasoned Behavior

Judge_Paul_GrimmThe power of emotions, and immediate gratification, should never be underestimated. This includes the positive motivators, like praise and recognition. An active judiciary can do much more to impact reasonable, ethical conduct than all appeals to reason. Judges need to be in your face, with both criticism and praise, stick and carrot. The motivations need to be immediate and real, not abstract and future oriented. See eg. Victor Stanley and the impact of Judge Grimm’s threats of immediate imprisonment of Pappas, the ultimate hide-the-ball litigant. Only that last jail contempt order in that case slowed the games. Victor Stanley, Inc. v. Creative Pipe, Inc., 269 F.R.D. 497, 506 (D. Md. 2010) (the attempts to collect the many fines and judgment entered in that case are still ongoing).

This all reminds me of Judge Waxse’s well-known quip that lawyers are like elementary particles, they change when observed (by judges). He has found that lawyers are more inclined to cooperate simply by including a possibility that a judge might someday watch a video of their behavior. Maybe we should require that all lawyer-to-lawyer communications be taped? Maybe we should triple the number of judges and give them all sensitivity training? Who knows? But the research shows that all manner of alternatives like that would be more successful than mere appeals to reason alone.


This all makes me wonder why I even bother to continue to write, but then again, you may have noticed that I try to include non-rational appeals in my writing, such as images, videos and the great irrational motivator of humor. Humor is an elusive emotion to reach, but well worth the effort. It is difficult to resist the ideas of anyone who makes you laugh. Personally, I refuse to emulate anyone who does not at least make me smile. If they make me laugh out loud, well, I will dig in deep to try to understand them and their ideas. (This is on reason I highly recommend the new self-help book by Scott Adams, the creator of Dilbert – How to Fail at Almost Everything and Still Win Big: Kind of the Story of My Life.)

Law is Like Economics: Both Are Still Based on an Irrational Reliance on Reason

Predictable_IrrationalAs you have seen from the videos, Dan Ariely is not only witty, but also a psychologist and an economist. He has one PhD in Psychology and another in Business Administration. He is also an author of a number of books that explain his works to the general reader, including the best seller: Predictably Irrational: The Hidden Forces That Shape Our Decisions.

Dan evaluates the implications of his irrationality findings in Psychology on the field of Economics. So too are many other pundits in the field. See eg. Post-Rational Economic Manand Exploring the Post-Rational 21st CenturyAriely and others have amassed a growing body of evidence that humans are not rational machines. Yet most economists, much like most lawyers, do not believe that. They still believe that people make rational decisions. For instance, that purchases are based on reason alone. See Rational Choice Theory. That is the basis of classic economic theory, and since that presumption is wrong, so is the theory. Economics is now struggling with the development of new theories based on the way people really act. Dan is a leader of that movement, which he calls Behavioral Economics.

Learning a little about Dan’s insights and proposals to reform economic theories, and make them more realistic, and empirically based, can provide insights into the Law and reforms we should make. Surely we can do better than propose more videotapes of lawyers, in your face judges, bibles and oaths, solemn court reporters, and British style ceremonial conduct. But these are a start.

Reasonable_guageMore fundamentally, we need to consider how we should speak of legal negligence in the future. We need to stop referring to whether an act is reasonable, and instead speak of acceptability, with reason just one of several factors to consider in evaluating acceptable behavior. That is what I call, for lack of a better term, Holistic Jurisprudence. More on that later. Perhaps some law professors and judges are already thinking and writing about this, and I am not aware of their writings. If not, then what are we waiting for? The evidence of innate irrationally based, yet acceptable, behavior, is strong. That is our everyday reality. So why do we use a measure of acceptable conduct that does not mirror reality? Legal theory needs to change as much as economic theory, and so too does legal practice.

Robots and Neuroscience?

Facc_RobotI know what some of you are thinking. Maybe the answer is simply to turn our justice system over to robots programmed to make rational decisions. They will not suffer from innate irrationality like our judges do. (Yes, even judges are human and thus even judges suffer from the same cognitive disorders, same irrational drivers, that other humans do). Rational machines could also be programmed to fairly consider the innate irrationality of humans. We could create super robojudges by using active machine learning. They could receive training in just-decision-making by our top judges. Imagine, for instance, the wisdom and wit of retired Judge Facciola programmed into an AI entity. The input from our top judges would thereby, in theory at least, live forever. The experience and intelligence of our best judges would then be available to all litigants, not just the lucky few who appear before them. This puts a while new positive spin onto the Ghost in the Machine image.

The AI enhanced robojudges would, of course, be far more than mere rational machines. They would be trained by our legal experts to render judgments based on the Whole Man, one that actually exists, and not the legal fiction of the Reasonable Man. They would be programmed in a post-rational manner following models of real human behavior of acceptable conduct. (Our best human judges and lawyers already do that anyway, even if the jurisprudence theory says otherwise.) The day may come when many litigants will prefer smart, well-trained robots to serve as judges to evaluate acceptable conduct, especially when there are good human appeals judges to oversee the process. That day is, however, still in the remote future.

ex_machinaOf course, if Ray Kurzweil is right about the Singularity coming soon, then all bets are off. But Kurzweil is probably wrong about how fast AI will advance, and so I do not see this anything like this happening this century. Moreover, the live forever proponents are, in my view, seriously deluded. (Fear of death tends to do that to people. Just look at the many wacky beliefs people have.) There is far more to the human soul than logic can ever replicate. We are more than a set of synapses that can be replicated with on off switches. Still, I could be wrong.

Automation by full human replication is not The answer (at least not in this century). The use of AI enhanced tools in the law, such as predictive coding for document review, is more realistic. It will continue and expand into many other legal activities. Very soon many more types of lawyers, in addition to contract review lawyers, will need to retool in order to stay employed. The simple logic and reason tasks of lawyers will be automated. All mere logic workers will have to change or be out of work. But, at the same time, new employment positions will open for those involved in the new technologies. The jobs that open up will require greater technology skills, intellect, empathy, leadership, creativity and imagination. They will require uniquely human attributes that are way beyond the programming of any robots, now and perhaps forever. Again see Only Humans Need Apply for a good review of this subject in the context of the economy.

I cannot imagine exactly how this will all play out, but, new advanced technologies will have to be part of all future legal reforms. Many of the technologies are probably still unknown and thus impossible to project. But some will be based on existing technologies, just significantly improved.

Facciola_computerPerhaps that will include active machine learning and AI based law clerks for judges. It is not hard to imagine a judge’s consideration of an AI enhanced suggested view of the case. After all, they already do this based on their clerk’s views. I suspect judicial clerks will be replaced way before the judges themselves. Judges need to be enhanced with better computers, not replaced by them. They need to be augmented, not automated.

To take a more mundane example than robots and AI, I suspect that lie detection technologies will soon advance enough to be of greater assistance to the Law. How about acceptably intrusive truth-compelling technologies? I can easily imagine neural nets with electronic brain monitors built into “truth hats.” Witnesses would be required to wear the truth-indicating hats and give the attorneys, judges and juries more and better insights into their testimony. Not only intentional lies could be revealed, but strength of recollection, areas of brain accessed, etc. This would not have to be dispositive, but suggestive. This could provide us with something more to evaluate credibility than raw instinct and intuition, as important as these faculties are.

Meet-the-Parents-lie-detector with Harry Potter twist

We should be looking for all kinds of ways to bring the recent incredible advances in Neuroscience into the justice system. This is not futuristic science fiction, nor my over-active imagination. It is already happening. Many neuroscientists are looking into lie detection and other possible neuroscience applications in the Law. See eg Harvard’s Center for Law, Brain and Behavior and its program on Lie Detection & the Neuroscience of Deception.

Final Word From Dan Ariely 

Dan-Ariely_WSJGetting back to Dan, in addition to teaching and running very clever experiments at MIT and Duke, Dan is the founder of an organization with a name that seems both funny and ironic, The Center for Advanced Hindsight. He is also a prolific writer and video maker, both activities I admire. See for instance his informative page at MIT, his blog at, his several books, and his videos, and even though its slightly boring, see his web page at Duke.

As a final piece of evidence on over reliance on reason I offer more testimony by Professor Ariely’s via another video, one which is not at all boring, I swear. It is called The Honest Truth About Dishonesty.

The video concludes with a subject near and dear to all lawyers, conflicts of interest. The non-rational impact of such conflicts turns out to be very strong and the law is wise to guard against them. Perhaps we should even step up our efforts in this area? 

Cornerstone Made of Pudding

CornerstoneThe scientific experiments of Dan Ariely and others show that the cornerstone of the Law – reasonability – is not made of granite as we had thought, it is made of pudding. You can hide your head in the sand, if you wish, and continue to believe otherwise. We humans are quite good at self-delusion. But that will not change the truth. That will not change quicksand into granite.

Our legal house needs a new and better foundation than reason. We must follow the physicists of a century ago. We must transcend Newtonian causality and embrace the more complex, more profound truth that science has revealed. The Reasonable Man is a myth that has outlived its usefulness. We need to accept the evidence, and move on. We need to develop new theories and propositions of law that confirm to the new facts at hand. Reason is just one part of who we are. There is much more to us then that: emotion, empathy, creativity, aesthetics, intuition, love, strength, courage, imagination, determination – to name just a few of our many qualities. These things are what make us uniquely human; they are what separate us from AI. Logic and reason may end up being the least of our abilities, although they are still qualities that I personally cherish.

Science has shown that our current reason-only-based system of justice is on shaky grounds. It is now up to us to do something about it. No big brother government, or super think-tank guru is going to fix this for us. Certainly not scientists either, but they should be able to help, along with technologists, programmers and engineers. This fix will have to come from within the legal profession itself. No one else knows our system of justice well enough to do this for us, certainly not scientists nor engineers. I know this from hard personal experience.

homer-simpson-brain-scanWhat are the implications of the findings of unreliable mental processes on the Law and our ability to reach just decisions? We should ask these questions concerning the Law, just like Professor Ariely is asking concerning Economics. Our fundamental legal assumption that all people can act out of reason and logic alone is false. Decisions made with these faculties alone are the exception, not the rule. There are a number of other contributing factors, including emotions, intuition, and environment. What does this mean to negligence law? To sanctions law? Now that the Reasonable Man is dead, who shall replace him?

Just as classical economic theory has had it all wrong, so too has classical legal theory. People are not built like reasonable machines. That includes lawyers, judges, and everyone else in the justice system, especially the litigants themselves.

If Not Reason, Then What?

Davinci_whole_manSince human reason is now known to be so unreliable, and is only a contributing factor to our decisions, on what should we base our legal jurisprudence? I believe that the Reasonable Man, now that he is known to be an impossible dream, should be replaced by the Whole Man. Our jurisprudence should be based on the reality that we are not robots, not mere thinking machines. We have many other faculties and capabilities beyond just logic and reason. We are more than math. We are living beings. Reason is just one of our many abilities.

So I propose a new, holistic model for the law. It would still include reason, but add our other faculties. It would incorporate our total self, all human attributes. We would include more than logic and reason to judge whether behavior is acceptable or not, to consider whether a resolution of a dispute is fair or not. Equity would regain equal importance.

A new schemata for a holistic jurisprudence would thus include not just human logic, but also human emotions, our feelings of fairness, our intuitions of what is right and just, and multiple environmental and perceptual factors. I suggest a new model start simple and use a four-fold structure like this, and please note I keep Reason on top, as I still strongly believe in its importance to the Law.


Some readers may notice that this model is similar to that of Carl Jung’s four personality types and the popular Myers Briggs personality tests. I am not advocating adoption of any of their ideologies, or personality theories, but I have over the years found their reference models to be useful. The above model, which is proposed only as a starting point for further discussion, is an extrapolation of these psychological models.

Call For Action

The legal profession needs to take action now to reduce our over-reliance on the Myth of the Reasonable Man. We should put the foundations of our legal system on something else, something more solid, more real than that. We need to put our house in order before it starts collapsing around us. That is the reasonable thing to do, but for that very reason we will not start to do it until we have better motivation than that. You cannot get people to act on reason alone, even lawyers. So let us engage the other more powerful motivators, including the emotions of fear and greed. For if we do not evolve our work to focus on far more than reason, then we will surely be replaced.


AI can think better and faster, and ultimately at a far lower cost. But can AI reassure a client? Can it tell what a client really wants and needs. Can AI think out of the box to come up with new, creative solutions. Can AI sense what is fair? Beyond application of the rules, can it attain the wisdom of justice. Does it know when rules should be bent and how far? Does it know, like any experienced judge knows, when rules should be broken entirely to attain a just result? Doubtful.

To get specific on the reforms needed now, we should bring back equity, and down play law. This was common in the first half of the Twentieth Century. At that time it was common to have Courts of Law and separate Courts of Equity. By the middle of the last century, Courts of Law won out in most states except Delaware, Mississippi, New Jersey, South Carolina, and Tennessee. Separate Equity Courts were closed down in favor of Courts of Law. Maybe we got it backwards. Maybe we were all led astray by our false confidence in reason.

Perhaps most courts should be Courts of Equity and Courts of Law become the exception. How has it worked out for the states that kept equity courts? Have Chancellors truly been able to side-step strict rules of law when they felt it was equitable to do so? If so, how has that worked out? Has power been abused? Or has justice been attained more often? What can we learn from chancery courts that might help us build a more holistic court of the future? We should apply analytics to study these questions to help us to reshape the law in a more human, holistic manner. Law Professors need to study this and help guide the profession.

A Few More Specific Suggestions of Reform

The AI enhancements already moving the law will continue to expand. That much is certain. Predictive coding, my speciality, is currently the prime example, but there will soon be many others.  They will enhance and improve our abilities. They will help us be more efficient. They will also help us to stay fair and honest. They could help end, or at least mitigate, human bias, stereotypes and prejudice.

Maybe timely reminders of ethics codes and serious under penalties of perjury type threats will also help? Maybe new, improved, and customized oaths will help? Oaths have been shown to be effective by Ariely’s research, so we should modify the rules accordingly. Let’s consider an update Rule 603 of the Federal Rules of Evidence.

Electrodes_EEG_RalphMaybe new truth recognition technologies should be used? Could a truth hat with built-in neural net be that far off? How about Google Glasses type apps that provide reliable new feedback of all kinds on the people you watch testifying? That cannot be too far off.  (The lie detection apps already on the market for iPhones, etc., all look bogus to me, which is not unexpected based on the limited biofeedback the phone sensors can provide.) Even if the information is not admissible as evidence, it could still be quite valuable to lawyers. Perhaps some of the recent discoveries in neuroscience could begin to be used in the justice system in all types of unexpected ways?

trophy_LawMaybe public recognition and awards to lawyers and judges who get it right will help? Ariely’s research suggests it will. And awards to litigants who do the right thing too, even if they lose the case? How about a discretionary set-off for defendants like that? How about the converse? Shame can be a powerful motivator too. Some judges already do this in subliminal manner. Let us encourage a more open application of emotion and creativity in judicial activity.

Maybe we should change the conditions and environments of places where witnesses are questioned, where mediations and trials are conducted? Maybe we should provide special training to court reporters on oath giving? Maybe we should have trials again, and not just settlements?

We need to look for all kinds of motivators. Knowledge and reason alone are not a solid foundation for justice. We also need wisdom. See eg. Losey, R., Information → Knowledge → Wisdom: Progression of Society in the Age of Computers.


Ralph_mosiac_7-16All social structures today are experiencing disruptive change, including the Law. Technology is driving these transformations. We need courage to face the reality of rapid change, instead of fearful avoidance. We need to shape the changes in the Law, not be overridden  by them. We need to be proactive, creative. We need to be guided by truth, not tradition. That means paying attention to analytics and psychology, and not just digging deeper into the Law alone for answers. The insights we need will come from a multidisciplinary approach, but one that is led by legal professionals. Only we truly understand legal practice. But our efforts to shape the change in our profession must include knowledge from all fields, including science, engineering and art. It must also include input from all other participants in the legal system, especially clients, litigants, plaintiffs, defendants. Legal practitioners, judges and scholars alone cannot provide a holistic view.

We must move away from over-reliance on reason alone. Our enlightened self-interest in continued employment in the rapidly advancing world of AI demand this. So too does our quest to improve our system of justice, to keep it current with the rapid changes in society.

Where we must still rely on reason, we should at the same time realize its limitations. We should look for new technology based methods to impose more checks and balances on reason than we already have. We should create new systems that will detect and correct the inevitable errors in reason that all humans make – lawyers, judges and witnesses alike. Bias and prejudice must be overcome in all areas of life, but especially in the justice system.

Computers, especially AI, should be able to help with this and also make the whole process more efficient. We need to start focusing on this, to make it a priority. It demands more than talk and thinking. It demands action. We cannot just think our way out of a prison of thought. We need to use all of our faculties, especially our imagination, creativity, intuition, empathy and good faith.

What Information Theory Tell Us About e-Discovery and the Projected ‘Information → Knowledge → Wisdom’ Transition

May 28, 2016

Ralph_and_LexieThis is an article on Information Theory, the Law, e-Discovery, Search and the evolution of our computer technology culture from Information → Knowledge → Wisdom. The article as usual assumes familiarity with writings on AI and the Law, especially active machine learning types of Legal Search. The article also assumes some familiarity with the scientific theory of Information as set forth in James Gleick’s book, The Information: a history, a theory, a flood (2011). I will begin the essay with several good instructional videos on Gleick’s book and Information Theory, including a bit about the life and work of the founder of Information Theory, Claude Shannon. Then I will provide my personal recapitulation of this theory and explore the application to two areas of my current work:

  1. The search for needles of relevant evidence in large, chaotic, electronic storage systems, such as email servers and email archives, in order to find the truth, the whole truth, and nothing but the truth needed to resolve competing claims of what happened – the facts – in the context of civil and criminal law suits and investigations.
  2. The articulation of a coherent social theory that makes sense of modern technological life, a theory that I summarize with the phrase: Information → Knowledge → Wisdom. See Information → Knowledge → Wisdom: Progression of Society in the Age of Computers and the more recent, How The 12 Predictions Are Doing That We Made In “Information → Knowledge → Wisdom.”

I essentially did the same thing in my blog last week applying Chaos Theories. What Chaos Theory Tell Us About e-Discovery and the Projected ‘Information → Knowledge → Wisdom’ Transition. This essay will, to some extent, build upon the last and so I suggest you read it first.

Information Theory

Gleick_The_InformationGleick’s The Information: a history, a theory, a flood covers the history of cybernetics, computer science, and the men and women involved with Information Theory over the last several decades. Gleick explains how these information scientists today think that everything is ultimately information. The entire Universe, matter and energy, life itself, is made up of information. Information in turn is ultimately binary, zeros and ones, on and off, yes and no. It is all bits and bytes.

Here are three videos, including two interviews of James Gleick, to provide a refresher on Information Theory for those who have not read his book recently. Information Wants to Have Meaning. Or Does It? (3:40, Big Think, 2014).

The Story of Information (3:47, 4th Estate Books, 2012).

Shannon_ClaudeThe generally accepted Father of Information Theory is Claude Shannon (1916-2001). He is a great visionary engineer whose ideas and inventions led to our current computer age. Among other things, he coined the word Bit in 1948 as the basic unit of information. He was also one of the first MIT hackers, in the original sense of the word as a tinkerer, who was always building new things. The following is a half-hour video by University of California Television (2008) that explains his life’s work and theories. It is worth taking the time to watch it.

Shannon was an unassuming genius, and like Mandelbrot, very quirky and interested in many different things in a wide variety of disciplines. Aside from being a great mathematician, Bell Labs engineer, and MIT professor, Shannon also studied game theory. He went beyond theory and devised several math based probability methods to win at certain games of chance, including card counting at blackjack. He collaborated with a friend at MIT, another mathematician, Edward Thorp, who became a professional gambler.

Shannon_movie_21_SpaceyShannon, his wife, and Thorp travelled regularly to Las Vegas for a couple of years in the early sixties where they constantly won at the tables using their math tricks, including card counting.  Shannon wanted to beat the roulette wheel too, but the system he and Thorp developed to do that required probability calculations beyond what he could do in his head. To solve this problem in 1961 he invented a small, concealable computer, the world’s first wearable computer, to help him calculate the odds. It was the size of a cigarette pack. His Law Vegas exploits became the very loose factual basis for a 2008 movie “21“, where Kevin Spacey played Shannon. (Poor movie, not worth watching.)

Shannon made even more money by applying his math abilities in the stock market. The list of his eclectic genius goes on and on, including his invention in 1950 of an electromechanical mouse named Theseus that could teach itself how to escape from a maze. Shannon’s mouse appears to have been the first artificial learning device. All that, and he was also an ardent juggler and builder/rider of little bitty unicycles (you cannot make this stuff up). Here is another good video of his life, and yet another to celebrate 2016 as the 100th year after his birth, The Shannon Centennial: 1100100 years of bits by the IEEE Information Theory Society.



For a different view loosely connected with Information Theory I recommend that you listen to an interesting Google Talk by Gleick.“The Information: A History, a Theory, a Flood” – Talks at Google (53:45, Google, 2011). It pertains to news and culture and the tension between a humanistic and mechanical approach, a difference that mirrors the tension between Information and Knowledge. This is a must read for all news readers, especially NY Times readers, and for everyone who consumes, filters, creates and curates Information (a Google term). This video has  a good dialogue concerning modern culture and search.

As you can see from the above Google Talk, a kind of Hybrid Multimodal approach seems to be in use in all advanced search. At Google they called it a “mixed-model.” The search tools are designed to filter identity-consonance in favor of diverse-harmonies. Crowd sourcing and algorithms function as curation authority to facilitate Google search. This is a kind of editing by omission that human news editors have been doing for centuries.

The mixed-model approach implied here has both human and AI editors working together to create new kinds of interactive search. Again, good search depends upon a combination of AI and human intelligence. Neither side should work alone and commercial interests should not be allowed to take control. Both humans and machines should create bits and transmit them. People should use AI software to refine their own searches as an ongoing process. This should be a conversation, an interactive Q&A. This should provide a way out of Information to Knowledge.

Lexington - IT lex

Personal Interpretation of Information Theory

My takeaway from the far out reaches of Information theories is that everything is information, even life. All living entities are essentially algorithms of information, including humans. We are intelligent programs capable of deciding yes or no, capable of conscious, intelligent action, binary code. Our ultimate function is to transform information, to process and connect otherwise cold, random data. That is the way most Information Theorists and their philosophers see it, although I am not sure I agree.

Life forms like us are said to stand as the counter-pole to the Second Law of Thermodynamics. The First Law you will remember is that energy cannot be created or destroyed. The Second Law is that the natural tendency of any isolated system is to degenerate into a more disordered state. The Second Law is concerned with the observed one-directional nature of all energy processes. For example, heat always flows spontaneously from hotter to colder bodies, and never the reverse, unless external work is performed on the system. The result is that entropy always increases with the flow of time.

Ludwig_BoltzmannThe Second Law is causality by multiplication, not a zig-zag Mandelbrot fractal division. See my last blog on Chaos Theory. Also see: the work of the Austrian Physicist, Ludwig Boltzmann (1844–1906) on gas-dynamical equations, and his famous H-theorem: the entropy of a gas prepared in a state of less than complete disorder must inevitably increase, as the gas molecules are allowed to collide. Boltzman’s theorem-proof assumed “molecular chaos,” or, as he put it, the Stosszahlansatz, where all particle velocities were completely uncorrelated, random, and did not follow from Newtonian dynamics. His proof of the Second Law was attacked based on the random state assumption and the so called Loschmidt’s paradox. The attacks from pre-Chaos, Newtonian dominated scientists, many of whom still did not even believe in atoms and molecules, contributed to Boltzman’s depression and, tragically, he hanged himself at age 62.

My personal interpretation of Information Theory is that humans, like all of life, counter-act and balance the Second Law. We do so by an organizing force called negentropy that balances out entropy. Complex algorithms like ourselves can recognize order in information, can make sense of it. Information can have meaning, but only by our apprehension of it. We hear the falling tree and thereby make it real.

This is what I mean by the transition from Information to Knowledge. Systems that have ability to process information, to bring order out of chaos, and attach meaning to information, embody that transition. Information is essentially dead, whereas Knowledge is living. Life itself is a kind of Information spun together and integrated into meaningful Knowledge.

privacy-vs-googleWe humans have the ability to process information, to find connections and meaning. We have created machines to help us to do that. We now have information systems – algorithms – that can learn, both on their own and with our help.  We humans also have the ability find things. We can search and filter to perceive the world in such a way as to comprehend its essential truth. To see through appearances, It is an essential survival skill. The unseen tiger is death. Now, in the Information Age, we have created machines to help us find things, help us see the hidden patterns.

We can create meaning, we can know the truth. Our machines, our robot friends, can help us in these pursuits. They can help us attain insights into the hidden order behind chaotic systems of otherwise meaningless information. Humans are negentropic to a high degree, probably more so than any other living system on this planet. With the help of our robot friends, humans can quickly populate the world with meaning and move beyond a mere Information Age. We can find order, process the binary yes-or-no choices and generate Knowledge. This is similar is the skilled editor’s function discussed in Gleick’s Talks at Google (53:45, Google, 2011), but one whose abilities are greatly enhanced by AI analytics and crowdsourcing. The arbitration of truth as they put it in the video is thereby facilitated.

With the help of computers our abilities to create Knowledge are exploding. We may survive the Information flood. Some day our Knowledge may evolve even further, into higher-level integrations – into Wisdom.

James GleickWhen James Gleick was interviewed by Publishers Weekly in 2011 about his book, The Information: a history, a theory, a floodhe touched upon the problem with Information:

By the technical definition, all information has a certain value, regardless of whether the message it conveys is true or false. A message could be complete nonsense, for example, and still take 1,000 bits. So while the technical definition has helped us become powerful users of information, it also instantly put us on thin ice, because everything we care about involves meaning, truth, and, ultimately, something like wisdom. And as we now flood the world with information, it becomes harder and harder to find meaning. That paradox is the final tension in my book.

Application of Information Theory to e-Discovery and Social Progress

Information-mag-glassIn responding to lawsuits we must search through information stored in computer systems. We are searching for information relevant to a dispute. This dispute always arises after the information was created and stored. We do not order and store information according to issues in a dispute or litigation that has not yet happened. This means that for purposes of litigation all information storage systems are inherently entropic, chaotic. They are always inadequately ordered, as far as the lawsuit is concerned. Even if the ESI storage is otherwise well-ordered, which in practice is very rare (think random stored PST files and personal email accounts), it is never well-ordered for a particular lawsuit.

As forensic evidence finders we must always sort through meaningless, irrelevant noise to find the meaningful, relevant information we need. The information we search is usually not completely random. There is some order to it, some meaning. There are, for instance, custodian and time parameters that assist our search for relevance. But the ESI we search is never presented to us arranged in an order that tracks the issues raised by the new lawsuit. The ESI we search is arranged according to other logic, if any at all.

It is our job to bring order to the chaos, meaning to the information, by separating the relevant information from the irrelevant information. We search and find the documents that have meaning for our case. We use sampling, metrics, and iteration to achieve our goals of precision and recall. Once we separate the relevant documents from the irrelevant, we attain some knowledge of the total dataset. We have completed First Pass Review, but our work is not finished. All of the relevant information found in the First Pass is not produced.

Additional information refinement is required. More yes-no decisions must be made in what is called Second Pass Review. Now we consider whether a relevant document is privileged and thus excluded from production, or whether portions of it must be redacted to protect confidentiality.

Even after our knowledge is so further enhanced by confidentiality sorting, and a production set is made, the documents produced, our work is still incomplete. There is almost always far too much information in the documents produced for them to be useful. The information must be further processed. Relevancy itself must be ranked. The relevant documents must be refined down to the 7 +/- 2 documents that will persuade the judge and jury to rule our way, to reach the yes or no decision we seek. The vast body of knowledge, relevant evidence, must become wisdom, must become persuasive evidence.


In a typical significant lawsuit the metrics of this process are as follows: from trillions, to thousands, to a handful. (You can change the numbers if you want to fit the dispute, but what counts here are the relative proportions.)

In a typical lawsuit today we begin with an information storage system that contains trillions of computer files. A competent e-discovery team is able to reduce this down to tens of thousands of files, maybe less, that are relevant. The actual count depends on many things, including issue complexity, cooperation and Rule 26(b)(1) factors. The step from trillions of files, to tens of thousands of relevant files, is the step from information to knowledge. Many think this is what e-discovery is all about: find the relevant evidence, convert Information to Knowledge. But it is not. It is just the first step: from 1 to 2. The next step, 2 to 3, the Wisdom step, is more difficult and far more important.

The tens of thousands of relevant evidence, the knowledge of the case, is still too vast to be useful. After all, the human brain can, at best, only keep seven items in mind at a time. Miller, The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information, Psychological Review 63 (2): 81–97. Tens of thousands of documents, or even thousands of documents, are not helpful to jurors. It may all be relevant, but is not all important. All trial lawyers will tell you that trials are won or lost by only five to nine documents. The rest is just noise, or soon forgotten foundation. Losey, Secrets of Search – Part III (5th secret).

The final step of information processing in e-discovery is only complete when the tens of thousands of files are winnowed down to 5 or 9 documents, or less. That is the final step of Information’s journey, the elevation from Knowledge to Wisdom.

Our challenge as e-discovery team members is to take raw information and turn it into wisdom – the five to nine documents with powerful meaning that will produce the favorable legal rulings that we seek. Testimony helps too of course, but without documents, it is difficult to test memory accuracy, much less veracity. This evidence journey mirrors the challenge of our whole culture, to avoid drowning in too-much-information, to rise above, to find Knowledge and, with luck, a few pearls of Wisdom.


Ralph_green2From trillions to a handful, from mere information to practical wisdom — that is the challenge of our culture today. On a recursive self-similar level, that is also the challenge of justice in the Information Age, the challenge of e-discovery. How to meet the challenges? How to self-organize from out of the chaos of too much information? The answer is iterative, cooperative, interactive, interdisciplinary team processes that employ advanced hybrid, multimodal technologies and sound human judgment. See What Chaos Theory Tell Us About e-Discovery and the Projected ‘Information → Knowledge → Wisdom’ Transition.

The micro-answer for cyber-investigators searching for evidence is fast becoming clear. It depends on a balanced hybrid application of human and artificial intelligence. What was once a novel invention, TAR, or technology assisted review, is rapidly becoming an obvious solution accepted in courts around the world. Rio Tinto PLC v. Vale S.A., 306 F.R.D. 125 (S.D.N.Y. 2015); Pyrrho Investments v MWB PropertyEWHC 256 (Ch) (2/26/16). That is how information works. What was novel one day, even absurd, can very quickly become commonplace. We are creating, transmitting and processing information faster than ever before. The bits are flying at a rate that even Claude Shannon would never have dreamed possible.

The pace of change quickens as information and communication grows. New information flows and inventions propagate. The encouragement of negentropic innovation – ordered bits – is the basis of our property laws and commerce. The right information at the right time has great value.

Just ask a trial lawyer armed with five powerful documents — five smoking guns. These essential core documents are what make or break a case. The rest is just so much background noise, relevant but unimportant. The smoking hot Wisdom is what counts, not Information, not even Knowledge, although they are, of course, necessary prerequisites. There is a significant difference between inspiration and wisdom. Real wisdom does not just appear out of thin air. It arises out of True Information and Knowledge.

The challenge of Culture, including Law and Justice in our Information Age, is to never lose sight of this fundamental truth, this fundamental pattern: Information → Knowledge → Wisdom. If we do, we will get lost in the details. We will drown in a flood of meaningless information. Either that, or we will progress, but not far enough. We will become lost in knowledge and suffer paralysis by analysis. We will know too much, know everything, except what to do. Yes or No. Binary action. The tree may fall, but we never hear it, so neither does the judge or jury. The power of the truth is denied,

There is deep knowledge to be gained from both Chaos and Information Theories that can be applied to the challenges. Some of the insights can be applied in legal search and other cyber investigations. Others can be applied in other areas. As shown in this essay, details are important, but never lose sight of the fundamental pattern. You are looking for the few key facts. Like the Mandelbrot Set they remain the same, or at least similar, over different scales of magnitude, from the small county court case, to the largest complex multinational actions. Each case is different, yet the same. The procedures ties them all together.

Meaning is the whole point of Information. Justice is whole point of the Law.

You find the truth of a legal controversy by finding the hidden order that ties together all of the bits of evidence together. You find the hidden meaning behind all of the apparent contradictory clues, a fractal link of the near infinite strings of bits and bytes.

What really happened? What is the just response, the equitable remedy? That is the ultimate meaning of e-discovery, to find the few significant, relevant facts in large chaotic systems, the facts that make or break your case, so that judges and juries can make the right call. Perhaps this is the ultimate meaning of many of life’s challenges? I do not have the wisdom yet to know, but, as Cat Stevens says, I’m on the road to find out.

What Chaos Theory Tell Us About e-Discovery and the Projected ‘Information → Knowledge → Wisdom’ Transition

May 20, 2016
Ralph and Gleick

Gleick & Losey meeting sometime in the future

This article assumes a general, non-technical familiarity with the scientific theory of Chaos. See James Gleick’s book, Chaos: making a new science (1987). This field of study is not usually discussed in the context of “The Law,” although there is a small body of literature outside of e-discovery. See: Chen, Jim, Complexity Theory in Legal Scholarship (Jurisdymanics 2006).

The article begins with a brief, personal recapitulation of the basic scientific theories of Chaos. I buttress my own synopsis with several good instructional videos. My explanation of the Mandelbrot Set and Complex numbers is a little long, I know, but you can skip over that and still understand all of the legal aspects. In this article I also explore the application of the Chaos theories to two areas of my current work:

  1. The search for needles of relevant evidence in large, chaotic, electronic storage systems, such as email servers and email archives, in order to find the truth, the whole truth, and nothing but the truth needed to resolve competing claims of what happened – the facts – in the context of civil and criminal law suits and investigations.
  2. The articulation of a coherent social theory that makes sense of modern technological life, a theory that I summarize with the words/symbols: Information → Knowledge → Wisdom. See Information → Knowledge → Wisdom: Progression of Society in the Age of Computers and the more recent, How The 12 Predictions Are Doing That We Made In “Information → Knowledge → Wisdom.”

Introduction to the Science of Chaos

Gleick’s book on Chaos provides a good introduction to the science of chaos and, even though written in 1987, is still a must read. For those who have read this long ago, like me, here is a good, short, 3:53, refresher video James Gleick on Chaos: Making a New Science (Open Road Media, 2011) below:

mandelbrot_youngA key leader in the Chaos Theory field is the late great French mathematician, Benoit Mandelbrot (1924-2010) (shown right). Benoit, a math genius who never learned the alphabet, spent most of his adult life employed by IBM. He discovered and named the natural phenomena of fractals. He discovered that there is a hidden order to any complex, seemingly chaotic system, including economics and the price of cotton. He also learned that this order was not causal and could not be predicted. He arrived at these insights by study of geometry, specifically the rough geometric shapes found everywhere in nature and mathematics, which he called fractals. The penultimate fractal he discovered now bears his name, The Mandelbrot Fractalshown in the computer photo below, and explained further in the video that follows.

Mandelbrot set

Look here for thousands of additional videos of fractals with zoom magnifications. You will see the recursive nature of self-similarity over varying scales of magnitude. The patterns repeat with slight variations. The complex patterns at the rough edges continue infinitely without repetition, much like Pi. They show the unpredictable element and the importance of initial conditions played out over time. The scale of the in-between dimensions can be measured. Metadata remains important in all investigations, legal or otherwise.


The Mandelbrot is based on a simple mathematical formula involving feedback and Complex Numbers: z ⇔ z2 + c. The ‘c’ in the formula stands for any Complex Number. Unlike all other numbers, such as the natural numbers one through nine –, the Complex Numbers do not exist on a horizontal number line. They exist only on an x-y coordinate time plane where regular numbers on the horizontal grid combine with so-called Imaginary Numbers on the vertical grid. A complex number is shown as c= a + bi, where a and b are real numbers and i is the imaginary number. Complex_number_illustration

A complex number can be visually represented as a pair of numbers (a, b) forming a vector on a diagram called an Argand diagram, representing the complex plane. “Re” is the real axis, “Im” is the imaginary axis, and i is the imaginary number. And that is all there is too it. Mandelbrot calls the formula embarrassingly simple. That is the Occam’s razor beauty of it.

To understand the full dynamics of all of this remember what Imaginary Numbers are. They are a special class of numbers where a negative times a negative creates a negative, not a positive, like is the rule with all other numbers. In other words, with imaginary numbers -2 times -2 = -4, not +4. Imaginary numbers are formally defined as i2 = −1.

Thus, the formula z ⇔ z2 + c, can be restated as z ⇔ z2 + (a + bi).

The Complex Numbers when iterated according to this simple formula – subject to constant feedback – produce the Mandelbrot set.


Mandelbrot_formulaThe value for z in the iteration always starts with zero. The ⇔ symbol stands for iteration, meaning the formula is repeated in a feedback loop. The end result of the last calculation becomes the beginning constant of the next: z² + c becomes the z in the next repetition. Z begins with zero and starts with different values for c. When you repeat the simple multiplication and addition formula millions of times, and plot it on a Cartesian grid, the Mandelbrot shape is revealed.

When iteration of a squaring process is applied to non-complex numbers the results are always known and predictable. For instance when any non-complex number greater than one is repeatedly squared, it quickly approaches infinity: 1.1 * 1.1 = 1.21 * 1.21 = 1.4641 * 1.4641 = 2.14358 and after ten iterations the number created is 2.43… * 10 which written out is 2,430,000,000,000,000,000,000,000,000,000,000,000,000,000. A number so large as to dwarf even the national debt. Mathematicians say of this size number that it is approaching infinity.

The same is true for any non-complex number which is less than one, but in reverse; it quickly goes to the infinitely small, the zero. For example with .9: .9.9=.81; .81.81=.6561; .6561.6561=.43046 and after only ten iterations it becomes 1.39…10 which written out is .0000000000000000000000000000000000000000000000139…, a very small number indeed.

With non-complex numbers, such as real, rational or natural numbers, the squaring iteration must always go to infinity unless the starting number is one. No matter how many times you square one, it will still equal one. But just the slightest bit more or less than one and the iteration of squaring will attract it to the infinitely large or small. The same behavior holds true for complex numbers: numbers just outside of the circle z = 1 on the complex plane will jump off into the infinitely large, complex numbers just inside z = 1 will quickly square into zero.

The magic comes by adding the constant c (a complex number) to the squaring process and starting from z at zero: z ⇔ z² + c. Then stable iterations – a set attracted to neither the infinitely small or infinitely large – become possible. The potentially stable Complex numbers lie both outside and inside of the circle of z = 1; specifically on the complex plane they lie between -2.4 and .8 on the real number line, the horizontal x grid, and between -1.2 and +1.2 on the imaginary line, the vertical y grid. These numbers are contained within the black of the Mandelbrot fractal.


In the Mandelbrot formula z ⇔ z² + c, where you always start the iterative process with z equals zero, and c equaling any complex number, an endless series of seemingly random or chaotic numbers are produced. Like the weather, the stock market and other chaotic systems, negligible changes in quantities, coupled with feedback, can produce unexpected chaotic effects. The behavior of the complex numbers thus mirrors the behavior of the real world where Chaos is obvious or lurks behind the most ordered of systems.

With some values of ‘c’ the iterative process immediately begins to exponentially increase or fall into infinity. These numbers are completely outside of the Mandelbrot set. With other values of ‘c’ the iterative process is stable for a number of repetitions, and only later in the dynamic process are they attracted to infinity. These are the unstable strange attractor numbers just on the outside edge of the Mandelbrot set. They are shown on computer graphics with colors or shades of grey according to the number of stable iterations. The values of ‘c’ which remain stable, repeating as a finite number forever, never attracted to infinity, and thus within the Mandelbrot set, are plotted as black.


Some iterations of complex numbers like 1 -1i run off into infinity from the start, just like all of the real numbers. Other complex numbers are always stable like -1 +0i. Other complex numbers stay stable for many iterations, and then only further into the process do they unpredictably begin to start to increase or decrease exponentially (for example, .37 +4i stays stable for 12 iterations). These are the numbers on the edge of inclusion of the stable numbers shown in black.

Chaos enters into the iteration because out of the potentially infinite number of complex numbers in the window of -2.4 to .8 along the horizontal real number axis, and -1.2 to 1.2 along the vertical imaginary number axis. There are an infinite subset of such numbers on the edge, and they cannot be predicted in advance. All that we know about these edge numbers is that if the z produced by any iteration lies outside of a circle with a radius of 2 on the complex plane, then the subsequent z values will go to infinity, and there is no need to continue the iteration process.

By using a computer you can escape the normal limitations of human time. You can try a very large number of different complex numbers and iterate them to see what kind they may be, finite or infinite. Under the Mandelbrot formula you start with z equals zero and then try different values for c. When a particular value of c is attracted to infinity – produces a value for z greater than 2 – then you stop that iteration, go back to z equals zero again, and try another c, and so on, over and over again, millions and millions of times as only a computer can do.

Mandel_zoom_08_satellite_antennaMandelbrot was the first to discover that by using zero as the base z for each iteration, and trying a large number of the possible complex numbers with a computer on a trial and error basis, that he could define the set of stable complex numbers graphically by plotting their location on the complex plane. This is exactly what the Mandelbrot figure is. Along with this discovery came the surprise realization of the beauty and fractal recursive nature of these numbers when displayed graphically.

The following Numberphile video by Holly Krieger, an NSF postdoctoral fellow and instructor at MIT, gives a fairly accessible, almost cutesy, yet still technically correct explanation to the Mandelbrot set.

Fractals and the Mandelbrot set are key parts of the Chaos theories, but there is much more to it than that. Chaos Theory impacts our basic Newtonian, cause-effect, linear world view of reality as a machine. For a refresher on the big picture of the Chaos insights and how the old linear, Newtonian, machine view of reality is wrong, look at this short summary: Chaos Theory (4:48)

Anther Chaos Theory instructional applying the insights to psychology is worth your view. The Science and Psychology of the Chaos Theory (8:59, 2008). It suggests the importance of spontaneous actions in the moment, the so-called flow state.

Also see High Anxieties – The Mathematics of Chaos (59:00, BBC 2008) concerning Chaos Theories, Economics and the Environment, and Order and Chaos (50:36, New Atlantis, 2015).

Application of Chaos Theories to e-Discovery

The use of feedback, iteration and algorithmic processes are central to work in electronic discovery. For instance, my search methods to find relevant evidence in chaotic systems follow iterative processes, including continuous, interactive, machine learning methods. I use these methods to find hidden patterns in the otherwise chaotic data. An overview of the methods I use in legal search is summarized in the following chart. As you can see, steps four, five and six iterate. These are the steps where human computer interactions take place. 

My methods place heavy reliance on these steps and on human-computer interaction, which I call a Hybrid process. Like Maura Grossman and Gordon Cormack, I rely heavily on high-ranking documents in this Hybrid process. The primary difference in our methods is that I do not begin to place a heavy reliance on high-ranking documents until after completing several rounds of other training methods. I call this four cylinder multimodal training. This is all part of the sixth step in the 8-step workflow chart above. The four cylinders search engines are: (1) high ranking, (2) midlevel ranking or uncertain, (3) random, and (4) multimodal (including all types of search, such as keyword) directed by humans.

Analogous Application of Similar Mandelbrot Formula For Purposes of Expressing the Importance of the Creative Human Component in Hybrid 


Recall Mandelbrot’s formula: z ⇔ z² + c, which is the same as z ⇔ z2 + (a + bi). I have something like that going on in my steps four, five and six. If you plugged the numbers of the steps into the Mandelbrot formula it would read something like this: 4 ⇔ 4² + (5+6i). The fourth step is the key AI Predictive Ranking step, where the algorithm ranks the probable relevance of all documents. The fourth step of computer ranking is the whole point of the formula, so AI Ranking here I will call ‘z‘ and represents the left side of the formula. The fifth step is where humans read documents to determine relevance, let’s call that ‘r‘ and the sixth step is where human’s train the computer, ‘t‘. This is the Hybrid Active Training step where the four cylinder multimodal training methods are used to select documents to train the whole set. The documents in steps five and six, r and t are added together for relevance feedback, (r + ti).

Thus, z ⇔ z² + c, which is the same as z ⇔ z2 + (a + bi), becomes under my system z ⇔ z + (r + ti). (Note: I took out the squaring, z², because there is no such exponential function in legal search; it’s all addition.) What, you might ask, is the i in my version of the formula? This is the critical part in my formula, just as it is in Mandelbrot’s. The imaginary number – i – in my formula version represents the creativity of the human conducting the training.

The Hybrid Active Training step is not fully automated in my system. I do not simply use the highest ranking documents to train, especially in the early rounds of training, as do some others. I use a variety of methods in my discretion, especially the multimodal search methods such a keywords, concept search, and the like. In text retrieval science this use of human discretion, human creativity and judgment, is called an ad hoc search. It contrasts with fully automated search, where the text retrieval experts try to eliminate the human element. See Mr EDR for more detail on 2016 TREC Total Recall Track that had both ad hoc and fully automated sections.

My work with legal search engines, especially predictive coding, has shown that new technologies do not work with the old methods and processes, such as linear review or keyword alone. New processes are required that employ new ways of thinking. The new methods that link creative human judgments (i) and the computer’s amazing abilities at text reading speed, consistency, analysis, learning and ranking (z).

A rather Fat Cat. My latest processes, Predictive Coding  3.0, are variations of Continuous Active Training (CAT) where steps four, five and six iterate until the project is concluded. Grossman & Cormack call this Continuous Active Learning or CAL, and they claim Trademark rights to CAL. I respect their right to do so (no doubt they grow weary of vendor rip-offs) and will try to avoid the acronym henceforth. My use of the acronym CAT essentially takes the view of the other side, the human side that trains, not the machine side that learns. In both Continuous Active Learning and CAT the machine keeps learning with every document that a human codes. Continuous Active Learning or Training, makes the linear seed-set method obsolete, along with the control set and random training documents. See Losey, Predictive Coding 3.0.

In my typical implementation of Continuous Active Training I do not automatically include every document coded as a training document. This is the sixth training step (‘t‘ in the prior formula). Instead of automatically using every document to train that has been coded relevant or irrelevant, I select particular documents that I decide to use to train. This, in addition to multimodal search in step six, Hybrid Active, is another way in which the equivalent of Imaginary Numbers come into my formula, the uniquely human element (ti). I typically use most every relevant document coded in step five, the ‘r‘ in the formula, as a training document, but not all. z ⇔ z + (r + ti)

I exercise my human judgment and experience to withhold certain training documents. (Note, I never withhold hot trainers (highly relevant documents)). I do this if my experience (I am tempted to say ‘my imagination‘) suggests that including them as training documents will likely slow down or confuse the algorithm, even if temporarily. I have found that this improves efficiency and effectiveness. It is one of the techniques I used to win document review contests.

robot-friendThis kind of intimate machine communication is possible because I carefully observe the impact of each set of training documents on the classifying algorithm, and carryover lessons – iterate – from one project to the next. I call this keeping a human in the loop and the attorney in charge of relevance scope adjudications. See Losey, Why the ‘Google Car’ Has No Place in Legal Search. We humans provide experienced observation, new feedback, different approaches, empathy, play and emotion. We also add a whole lot of other things too. The AI-Robot is the Knowledge fountain. We are the Wisdom fountain.That it is why we should strive to progress into and through the Knowledge stage as soon as possible. We will thrive in the end-goal Wisdom state.

Application of Chaos Theory to Information→Knowledge→Wisdom

mininformation_arrowsThe first Information stage of the post-computer society in which we live is obviously chaotic. It is like the disconnected numbers that lie completely outside of the Mandelbrot set. It is pure information with only haphazard meaning. It is often just misinformation. Just exponential. There is an overwhelming deluge of such raw information, raw data, that spirals off into an infinity of dead-ends. It leads no where and is disconnected. The information is useless. You may be informed, but to no end. That is modern life in the post-PC era.

The next stage of society we seek, a Knowledge based culture, is geometrically similar to the large black blogs that unite most of the figure. This is the finite set of numbers that provide all connectivity in the Mandelbrot set. Analogously, this will be a time when many loose-ends will be discarded, false theories abandoned, and consensus arise.

In the next stage we will not only be informed, we will be knowledgable. The information we all be processed. The future Knowledge Society will be static, responsible, serious and well fed. People will be brought together by common knowledge. There will be large scale agreements on most subjects. A tremendous amount of diversity will likely be lost.

After a while a knowledgable world will become boring. Ask any professor or academic.  The danger of the next stage will be stagnation, complacency, self-satisfaction. The smug complacency of a know-it-all world. This may be just as dangerous as the pure-chaos Information world in which we now live.

If society is to continue to evolve after that, we will need to move beyond mere Knowledge. We will need to challenge ourselves to attain new, creative applications of Knowledge. We will need to move beyond Knowledge into Wisdom.

benoit-mandelbrot-seahorse-valleyI am inclined to think that if we ever do progress to a Wisdom-based society, we will be a place and time much like the unpredictable fractal edges of the Mandelbrot. Stable to a point, but ultimately unpredictable, constantly changing, evolving. The basic patterns of our truth will remain the same, but they will constantly evolve and be refined. The deeper we dig, the more complex and beautiful it will be. The dry sameness of a Knowledgable based world will be replaced by an ever-changing flow, by more and more diversity and individuality. Our social cohesivity will arise from recursivity and similarity, not sameness and conformity. A Wisdom based society will be filled with fractal beauty. It will live ever zigzagging between the edge of the known and unknown. It will also necessarily have to be a time when people learn to get along together and share in prosperity and health, both physical and mental. It will be a time when people are accustomed to ambiguities and comfortable with them.

In Wisdom World knowledge itself will be plentiful, but will be held very lightly. It will be subject to constant reevaluation. Living in Wisdom will be like living on the rough edge of the Mandelbrot. It will be a culture that knows infinity firsthand. An open, peaceful, ecumenical culture that knows everything and nothing at the same time. A culture where most of the people, or at least a strong minority, have attained a certain level of personal Wisdom.


Back to our times, where we are just now discovering what machine learning can do, we are just beginning to pattern our investigations, our search for truth, in the Law and elsewhere, on new information gleaned from the Chaos theories. Active machine learning, Predictive Coding, is a natural outgrowth of Chaos Theory and the Mandelbrot Set. The insights of hidden fractal order that can only be seen by repetitive computer processes are prevalent in computer based culture. These iterative, computer assisted processes have been the driving force behind thousands of fact investigations that I have conducted since 1980.

I have been using computers to help me in legal investigations since 1980. The reliance on computers at first increased slowly, but steadily. Then from about 2006 to 2013 the increase accelerated and peaked in late 2013. The shift is beginning to level off. We are still heavily dependent on computers, but now we understand that human methods are just as important as software. Software is limited in its capacities without human additive, especially in legal search. Hybrid, Man and Machine, that is the solution. But remember that the focus should be on us, human lawyers and search experts. The AIs we are creating and training should be used to Augment and Enhance our abilities, not replace them. They should complement and complete us.

butterfly_effectThe converse realization of Chaos Theory, that disorder underlies all apparent order, that if you look closely enough, you will find it, also informs our truth-seeking investigatory work. There are no smooth edges. It is all rough. If you look close enough the border of any coastline is infinite.

The same is true of the complexity of any investigation. As every experienced lawyer knows, there is no black and white, no straight line. It always depends on so many things. Complexity and ambiguity are everywhere. There is always a mess, always rough edges. That is what makes the pursuit of truth so interesting. Just when you think you have it, the turbulent echo of another butterfly’s wings knock you about.

The various zigs and zags of e-discovery, and other investigative, truth-seeking activities, are what make them fascinating. Each case is different, unique, yet the same patterns are seen again and again with recursive similarity. Often you begin a search only to have it quickly burn out. No problem, try again. Go back to square one, back to zero, and try another complex number, another clue. Pursue a new idea, a new connection. You chase down all reasonable leads, understanding that many of them will lead nowhere. Even failed searches rule out negatives and so help in the investigation. Lawyers often try to prove a negative.

The fractal story that emerges from Hybrid Multimodal search is often unexpected. As the search matures you see a bigger story, a previously hidden truth. A continuity emerges that connects previously unrelated facts. You literally connect the dots. The unknown complex numbers – (a + bi) – the ones that do not spiral off into the infinite large or small, do in fact touch each other when you look closely enough at the spaces.

z ⇔ z2 + (a + bi)

SherlockI am no Sherlock, but I know how to find ESI using computer processes. It requires an iterative sorting processes, a hybrid multimodal process, using the latest computers and software. This process allows you to harness the infinite patience, analytics and speed of a machine to enhance your own intelligence ……. to augment your own abilities. You let the computer do the boring bits, the drudgery, while you do the creative parts.

The strength comes from the hybrid synergy. It comes from exploring the rough edges of what you think you know about the evidence. It does not come from linear review, nor simple keyword cause-effect. Evidence is always complex, always derived from chaotic systems. A full multimodal selection of search tools is needed to find this kind of dark data.

The truth is out there, but sometimes you have to look very carefully to find it. You have to dig deep and keep on looking to find the missing pieces, to move from Information → Knowledge → Wisdom.













blue zoom Mandelbrot fractal animation of looking deeper into the details



e-Discovery Team’s Best Practices Education Program

May 8, 2016


EDBP                   Mr.EDR         Predictive Coding 3.0
59 TAR Articles
Doc Review  Videos



e-Discovery Team Training

Information → Knowledge → Wisdom

Ralph_4-25-16Education is the clearest path from Information to Knowledge in all fields of contemporary culture, including electronic discovery. The above links take you to the key components of the best-practices teaching program I have been working on since 2006. It is my hope that these education programs will help move the Law out of the dangerous information flood, where it is now drowning, to a safer refuge of knowledge. Information → Knowledge → Wisdom: Progression of Society in the Age of Computers; and How The 12 Predictions Are Doing That We Made In “Information → Knowledge → Wisdom.” For more of my thoughts on e-discovery education, see the e-Discovery Team School Page.

justice_guage_negligenceThe best practices and general educational curriculum that I have developed over the years focuses on the legal services provided by attorneys. The non-legal, engineering and project management practices of e-discovery vendors are only collaterally mentioned. They are important too, but students have the EDRM and other commercial organizations and certifications for that. Vendors are part of any e-Discovery Team, but the programs I have developed are intended for law firms and corporate law departments.

LIFE_magazine_Losey_acceleratesThe e-Discovery Team program, both general educational and legal best-practices, is online and available 24/7. It uses lots of imagination, creative mixes, symbols, photos, hyperlinks, interactive comments, polls, tweets, posts, news, charts, drawings, videos, video lectures, slide lectures, video skits, video slide shows, music, animations, cartoons, humor, stories, cultural themes and analogies, inside baseball references, rants, opinions, bad jokes, questions, homework assignments, word-clouds, links for further research, a touch of math, and every lawyer’s favorite tools: words (lots of them), logic, arguments, case law and precedent.

All of this to try to take the e-Discovery Team approach from just information to knowledge →. In spite of these efforts, most of the legal community still does not know e-discovery very well. What they do know is often misinformation. Scenes like the following in a law firm lit-support department are all too common.

supervising-tipsThe e-Discovery Team’s education program has an emphasis on document review. That is because the fees for lawyers reviewing documents is by far the most expensive part of e-discovery, even when contract lawyers are used. The lawyer review fees, and review supervision fees, including SME fees, have always been much more costly than all vendor costs and expenses put together. Still, the latest AI technologies, especially active machine learning using our Predictive Coding 3.0 methods, are now making it possible to significantly reduce review fees. We believe this is a critical application of best practices. The three steps we identify for this area in the EDBP chart are shown in green, to signify money. The reference to C.A. Review is to Computer Assisted Review or CAR, using our Hybrid Multimodal methods.



Predictive Coding 3.0 Hybrid Multimodal Document Search and Review

Control-SetsOur new version 3.0 techniques for predictive coding makes it far easier than ever before to include AI in a document review project. The secret control set has been eliminated, so too has the seed set and SMEs wasting their time reviewing random samples of mostly irrelevant junk. It is a much simpler technique now, although we still call it Hybrid Multimodal.

robot-friendHybrid is a reference to the Man/Machine interactive nature of our methods. A skilled attorney uses a type of continuous active learning to train an AI to help them to find the documents they are looking for. This Hybrid method greatly augments the speed and accuracy of the human attorneys in charge. This leads to cost savings and improved recall. A lawyer with an AI helper at their side is far more effective than lawyers working on their own. This means that every e-discovery team today could use a robot like Kroll Ontrack’s Mr. EDR to help them to do document review.

Search_pyramidMultimodal is a reference to the use of a variety of search methods to find target documents, including, but not limited to, predictive coding type ranked searches. We encourage humans in the loop running a variety of searches of their own invention, especially at the beginning of a project. This always makes for a quick start in finding relevant and hot documents. Why the ‘Google Car’ Has No Place in Legal Search. The multimodal approach also makes for precise, efficient reviews with broad scope. The latest active machine learning software when fully integrated with a full suite of other search tools is attaining higher levels of recall than ever before. That is one reason Why I Love Predictive Coding.

Mr_EDRI have found that Kroll Ontrack’s EDR software is ideally suited for these Hybrid, Multimodal techniques. Try using it on your next large project and see for yourself. The Kroll Ontrack consultant specialists in predictive coding, Jim and Tony, have been trained in this method (and many others). They are well qualified to assist you in every step of the way and their rates are reasonable. With you calling the shots on relevancy, they can do most of the search work for you and still save your client’s money. If the matter is big and important enough, then, if I have a time opening, and it clears my firm’s conflicts, I can also be brought in for a full turn-key operation. Whether you want to include extra time for training your best experts is your option, but our preference.



Embrace e-Discovery Team Education to Escape Information Overload



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