Optimizing Shubh Laabh: Harmonious Profitability

Sustainable machine-generated, data-driven decisions
The growing popularity of 'Big Data' coupled with 'machine learning' techniques coincides with an increasing use of automated, machine-computed solutions for a a variety of business problems that were once solved and optimized based (predominantly) on human inputs. Machine-generated solutions have been shown to be superior to these previous methods on the measured performance metrics in many instances, and companies all over the globe have deployed advanced analytics and business optimization models (e.g. built using Operations Research) to achieve incremental profitability, cost reductions, or improved system efficiency. However, all is not well. Some solutions are sustainable, and work well over time, while others begin to run into a seemingly endless stream of human or environmental issues, and fall by the wayside. 

What differentiates sustainable machine-generated optimizations from the unsustainable ones? The answer is not straightforward, and this post explores one aspect. For an example of what kinds of issues can crop up, see this BBC news article: "Amazon workers face increased risk of mental illness", as well as this older article on 'unhappy truckers'. A portion of the BBC article highlighted below is color-coded to show where sustainable decision optimization could be potentially applied to improve upon the status-quo):

 "..... Amazon said the safety of its workers was its "number one priority."

Undercover reporter Adam Littler, 23, got an agency job at Amazon's Swansea warehouse. He took a hidden camera inside for BBC Panorama to record what happened on his shifts.

He was employed as a "picker", collecting orders from 800,000 sq ft of storage.

A handset told him what to collect and put on his trolley. It allotted him a set number of seconds to find each product and counted down. If he made a mistake the scanner beeped.

"We are machines, we are robots, we plug our scanner in, we're holding it, but we might as well be plugging it into ourselves", he said.

"We don't think for ourselves, maybe they don't trust us to think for ourselves as human beings, I don't know.

..... Prof Marmot, one of Britain's leading experts on stress at work, said the working conditions at the warehouse are "all the bad stuff at once".

He said: "The characteristics of this type of job, the evidence shows increased risk of mental illness and physical illness."

"There are always going to be menial jobs, but we can make them better or worse. And it seems to me the demands of efficiency at the cost of individual's health and wellbeing - it's got to be balanced." "

I spent the early-mid 2000s redesigning, and improving airline crew scheduling optimization systems. This period also happened to be the industry's most tumultuous: 9-11, out-of-control fuel and labor costs exacerbated by the invasion of Iraq, repeated strikes by various worker unions followed by contentious negotiations that lead to multiple CBAs (collective bargaining agreements) being ripped up and rewritten, and companies lining up to file for Chapter-11 bankruptcy protection, etc. Endless problems. The office atmosphere got quite intense when the R&D team somehow managed to find itself in the middle of these events, and facing heat from all sides (management, unions, soaring passenger complaints) on the kinds of solutions that were generated by our decision support systems (The US airline industry pioneered the use of such techniques). The analytical lessons empirically learnt from such episodes are hard to replicate in classrooms. One such lesson was "pay a lot of attention to the impact of your model on the people and environment". The application of this lesson has been explored in this space in a variety of contexts earlier: here (Gandhi's methods), here (Smart-Grid), here (Airline Crew scheduling), and here (Conflict resolution). The issue is revisited here by borrowing an idea from traditional Indian business philosophy to see if new insight can be generated toward answering our question on sustainable business optimization.

(pic link source: http://www.indiabazaar.co.uk)

(Updated: November 30, 2013 Finally found the link to article that inspired this post)
It is interesting to note that for centuries, traditional business communities in India had adopted the policy of Shubh Laabh (written in Hindi in the picture), which roughly translates into 'auspicious/harmonious profit' (Aravindan Neelakandan, co-author of 'Breaking India' in the linked article notes: "Lakshmi symbolizes the wealth that is holistic: it is wealth that puts welfare (Shub) before profit (Laabh)." The pursuit of wealth and profitability was never frowned upon in Hindu society, while unconstrained profit maximization was recognized as a socially destabilizing and ecologically unsustainable objective.  'Shubh Laabh' recognizes and respects the presence of long-lasting and latent side-effects that arise from business decisions (that can bring you 'bad luck') and attempts to balance them equitably with the more immediate goal of profitability (Laabh). These traditional businesses employed some operational form of Ahimsa (the principle of minimal harm) to optimize Shubh Laabh:

Rule a) limit harm (hard-constraint version)

Rule b) minimize harm (soft-constraint version)

Let us see how this idea can be incorporated within modern decision optimization systems. Amazon appears to have satisfied all legal requirements via (a) by making safety a top priority. It has probably ensured that the statistical rate of accidents is below some stringent threshold. In the airline world, (a) is achieved by ensuring total compliance with respect to all FAA- and CBA-mandated safety rules. However, this represents a necessary condition that tolerates a certain level of error as 'legally acceptable collateral damage'. The resultant formulation is: maximize profitability subject to safety regulations. However, this in itself is an insufficient specification if we want our algorithms to minimize harmful side-effects. An Ahimsa-based model would additionally consider (b) and eschew profit achieved at the cost of a reduced employee quality-of-work-life (QWL) or environmental degradation, as unsustainable and counterproductive in the long run. 

For large-scale systems such as a retail supply-chain or airline crew schedules, a reasonably skilled analytics professional should be able to incorporate requirements (a)-(b) within their decision support algorithm which, among alternative near-optimal solutions (and there are often many of these), selects one that also maximizes worker QWL, and/or minimizes harm (e.g. reduces carbon footprint). This requires the human-and-environment-variables in the system be treated positively as an active and equal partner based on mutual respect, by explicitly including their requirements as part of the primary goal (objective function), going beyond a legalistic/adversarial approach of treating these variables as a 'loss-making noise that has to be managed' by specifying a minimum tolerance constraint.

To summarize
It is possible to achieve sustainable profitable solutions via automated decision support systems that are also harmonious and sustainable, by paying due respect to all the stakeholders (including Ms. Nature), right from the design phase.

An old blog discussed Rajiv Malhotra's use of 'mutual respect' (as opposed to mere tolerance) as a simple but powerful basis for two heterogeneous groups of people, or people subscribing to conflicting thought systems, to achieve a fair and sustainable equilibrium in their interactions. It appears that such a mutual respect:

a) is implicitly present in the idea of Shubh Laabh, which in turn

b) can be employed as a key guiding principle of 'sustainable design' when building decision support algorithms for managing complex business problems, where multiple, and potentially conflicting, goals have to be delicately balanced.

The opinions expressed in this article are personal.

Operations Research for the SmartGrid - 1

A popular joke in my undergrad campus at IIT-Madras used to be "why is the large water tank in our campus not used? Answer: the design engineers did not take the weight of water into account". The legend may be as real as the croc in the campus lake, but newspaper reports a few days ago quoted a US government spokesperson saying that the 'heath insurance website worked correctly, but just did not take the volume into account'. I'm sure a lot more attention was paid to the voters within the sophisticated analytical models used during the 2012 elections. Volume was not a problem then, somehow. Actions reflect priorities, as Gandhi said. So what are the priority areas in Smart-Grid research?

I recently attended the IEEE SmartGridComm 2013 international conference in the beautiful city of Vancouver, Canada. (A very brief historical tangent: From my Indian-American immigrant perspective, Vancouver is also a somber reminder of the discrimination that was once practiced by the US and Canadian governments, exemplified by the Komagata Maru incident). The paper presentations were refereed entries, uniformly of high quality, and largely focused on the dizzying science and technology associated with the various elements of the smart-grid (electric vehicles, batteries, wind, solar, communications, security, ...). Marry this with 'Big Data' and you get the convoluted buzz of two 'hyperbolic' distributions. Personally speaking, the glaring problem was this: the tech part felt overcooked, and the human part, somewhat overlooked, save for this five-minute talk, and the excellent keynote talks, which emphasized the latter (a favorite keynote comment described the important and immediate practical problem of 'transmission optimization' as the drunken uncle of the smart grid - largely ignored, but full of smart ideas). I found that several others at the conference too shared an opinion: the single most important component of the Smart-Grid remains the people for whom it is being built in the first place. If anything, understanding their behavior and impact is more important than ever before.

The world of electrical system modeling is full of elegant math that manage electrons that flow through circuits obediently as dictated by the equations. These models match up relatively well with reality (even imaginary numbers work here). In contrast, real world ORMS projects usually begin with people's real and changing requirements, and culminates in finding lasting solutions for real people, using noisy and incomplete SmallData. Unlike widgets, packets, and electrons, the goal of accurately modeling human response largely remains an open challenge, and the temptation to simply ignore this component of the SmartGrid is strong. However, the empirical, perhaps paradoxical, lesson I've learned the hard way is that the more effectively we want to mechanize, automate, and optimize systems by reducing or eliminating manual intervention (i.e. save humans from humans, a la Asimov's robots), the more practically important it becomes for our optimization models to take into account the behavior of, and the implications for all the human stakeholders, upfront. Be it workforce scheduling, Big data analytics, or the SmartGrid, an ahimsa-based multi-objective approach that also minimizes harm or maximizes benefit to the human element and blends harmoniously with the environment is likely to be more sustainable. Which is another way of saying: SmartGrid is one heck of an OR opportunity and I'm glad to be a small part of this journey.

The next part of this series will review some interesting SmartGrid optimization problems.

Gandhi and Operations Research

October 2nd is the birthday of Mahatma Gandhi, a major spiritual force behind the Indian freedom movement of the 20th century. Gandhi-ji also was a fundamental and direct inspiration for Martin Luther King Jr.'s civil rights movement of the 1960s, and Nelson Mandela's struggle against apartheid. In this post, we attempt to examine his idea of ahimsa from an optimization perspective.

Update (Oct 5): This Huffington Post article provides amazing insight into Gandhi's ideas.

What is ahimsa?
Indian textbooks mention that Gandhiji brought the colonial empire in India to its knees by using ahimsa and sathyagraha (both were 'spelling bee' words a couple of years ago). These words have no equivalent in English, and are often used to imply "passive resistance", "pacifism", or "non-violence". A mathematical optimization model provides a more useful translation.

The popular Sanskrit verses on himsa and ahimsa given below was popularized by Gandhiji:
ahimsa paramo dharmaha,
dharma himsa tathaiva cha
[Oct 2018 update: the second verse has been attributed to Swami Chinmayananda)

My translation:
Non-harming is the greatest virtue;
So too is righteous harm.

The second line suggests that allowing cruelty to go unchallenged is equivalent to willingly permitting harm, and therefore, must be resisted. The verses are a combination of the ideal (global optimality = zero harm), and a context-dependent violation of that ideal (soft rule = minimize harm). 'Local harming' is permissible in rare circumstances when it results in an overall reduction in global harm. The gangrenous foot has to be amputated to save the body, or a terrorist who attacks innocents in a mall or a school has to be taken down by security forces. In a recent talk, Narendra Modi tells us a story of how Gandhi would request his assistant at Sabarmati Ashram to pour back half a cup of water back into the river, because all he wanted was half a cup. Minimal harm!

(updated October 2)
Optimization Model of Ahimsa
From an optimization modeling perspective, these ahimsa verses represents an objective function of minimizing harm. In normal circumstances, the optimal value should be zero, but in all circumstances, it should be minimal. When some non-zero harm is inevitable, the goal is to limit the total harm to a minimum, i.e., the employed level of harm is optimal if and only if it is necessary and sufficient to restore dharma. The 'necessary' condition implies minimalism of the counteracting harm, while the 'sufficient' condition implies the safe neutralization of the source of the harm. It's a tough balancing act for humans even though nature itself effortlessly adheres to Newton's third law. A pure hard-constraint version of ahimsa would discourage self-defense and even celebrate cowardice, while a pure soft constraint version could open the doors to unnecessary use of force, and justifying cost versus benefit approaches. (The legal system dictum of "let a hundred guilty go unpunished, but a single innocent should not be wrongly convicted" is an interesting case study in this regard.) Hence, applying any one of these two lines is an incomplete specification and can lead to unpredictable results.

We argued a while ago that these verses are an improved 'fail-safe' choice for the 'zeroth law' of robotics. In the real world, when we build decision models to aid decision making, we can optimize decision variables to pick a pareto-optimal solution that also results in the least disruption to the system ("don't fix what isn't broken"). For example, if we are scheduling workers to maximize efficiency or minimizing cost, then an optimal solution that also minimally disrupts (and preferably, enhances) their quality-of-work-life is more likely to be sustainable over the long run.

Gandhiji's Swaraj
Many feel that Gandhiji was partial to the first line, and quotes attributed to him support this claim. On the other hand, Gandhi's 'Hind Swaraj' and his lesser known quotes on preferring violent self-defense to cowardly capitulation suggests that he was aware of both verses. His book 'Hind Swaraj' (Indian self-rule) implies that his primary objective was not merely an overthrow of colonizers, but to achieve the strategic and deeper goal of ending the cultural genocide of India (restoring its Sanskriti and dharma). Applying the ahimsa verses would yield a path to Swaraj that results in minimal incremental harm to India's Sanskriti and dharma. Such a path may not necessarily also be optimal in terms of being the shortest-time path, or the least painful, or one that maximizes regained territory.

Conflict Resolution - 3: Contextual Optimization

Asimov's Zeroth Law
We continue this discussion from where we left off a few weeks ago: robot-ethics and how Asimov's robots resolved conflicts. A key update to Asimov's original three laws is the inclusion of the zeroth law (from Wikipedia):

"A robot may not harm humanity, or, by inaction, allow humanity to come to harm."

A fundamental difference between this law and the others is its abstract specification, with little clue on how it will be implemented. Furthermore, by giving this law the highest priority, Asenion robots are designed to first and foremost safeguard 'humanity', while also minimizing injury to individual humans and themselves as secondary and tertiary objectives. A robot is given the difficult computational task of proving that the cost of hurting a human is less than humanitarian benefit derived from an alternative action, and must do so within a finite amount of time.

The Universal Conflict-Resolution Model
Immanuel Kant's 'categorical imperative' is an example of an universal conflict resolution model that furiously strives to be context-free, and one that has greatly influenced western thought. Similarly, the Ten-Commandments' "Thou shall not kill" is absolute. Any machine that includes this hard constraint would be unable to kill, even defensively in order to protect a large number of humans under threat. Kantian rules are easy to 'encode-and-forget' within machines and systems since one does not have to ever worry about the context of its application. As we saw in the previous post, the robotic laws are context-free and Kantian in design. The original three laws operated as hard, must-satisfy constraints and necessary conditions. Per Wikipedia, Kant's

"perfect duties are those that are blameworthy if not met, as they are a basic required duty for a human being."

The problem of course is that (see prior post), the rigidity of all-hard rules is not practical and later versions of Asenion robots appear to additionally operate based on the concept of Kant's imperfect duty (again from Wikipedia):

"unlike perfect duties, you do not attract blame should you not complete an imperfect duty but you shall receive praise for it should you complete it, as you have gone beyond the basic duties and taken duty upon yourself"

Thus, imperfect duties are soft, rather than hard constraints, and the aim is to maximally satisfy (minimally violate) requirements. However, the optimization weights that trade off the degree of importance assigned to each of the Asenion robot's 'imperfect duties' are hard-coded, and additional context-specific inputs are required from humans to achieve a satisfactory result in resolving a dilemma. The robots have no authority to perform context-specific conflict resolutions on their own.

How then does this zeroth law practically work in Asimov stories? It's Kantian abstraction is incomprehensible to all except a couple of enlightened robots (with telepathic ability, no less). In general, the zeroth law remains useful only on paper for most of the robots.

Contextual Ethics: The Indian Way
Rajiv Malhotra's path-breaking book "Being Different: Indian Challenge to Western Universalism" provides a fascinating contrast between the traditional Indian approach of 'contextual ethics' (CE) that arose from its Dharmic thought system, and the 'context free ethics' that largely guides the western approach (for an earlier post based on his work, see here. Some of the methods in this post are applications of ideas in this book). From an optimization perspective, we can think of a CE-embedded robot as one that maximally satisfies a combination of hard, soft, and firm constraints, where a 'firm' constraint refer to a hard constraint that is minimally and temporarily relaxed, depending on the specific context of the dilemma, and for the benefit of the 'greatest good', at the expense of incurring a context-specific penalty. This flexibility must not be confused with moral relativism - where a set of soft constraints are tactically and optimally manipulated according to context to maximize some convenient self-serving objective. 'Optimally timing an apology' can be thought of as an example of a non self-serving, contextual optimization model.  It is worth doing a deep dive into this concept, by reviewing some passages in the aforementioned book:

"The frequently leveled charge of moral relativism against this contextual morality is inaccurate, because the conduct and motive are considered consequential in judging the ultimate value of statements.

.... Dharmic ethics are formulated in response to the situation and context of the problem in a way that makes Western ethics seem unduly codified, rigid, monolithic and even simplistic. A.K. Ramanujan, in his influential essay 'Is There an Indian Way of Thinking?', uses the terms 'context-free' and 'context-sensitive' to contrast the West and India in their respective approaches to ethics: "Cultures may be said to have overall tendencies to idealize, and think in terms of, either the context-free or the context-sensitive kind of rules. Actual behavior may be more complex, though the rules they think with are a crucial factor in guiding the behavior. In cultures like India's, the context-sensitive kind of rule is the preferred formulation" ....

.... Dharmic traditions, on the other hand, have long sought to arrive at truth by balancing universal truths and acts with those that can be determined only in the context in which they occur. Dharmic cultures have thus evolved to become comfortable with complexity and nuance, rejecting notions of the absolute and rigid ideals of morality and conduct....

....dharmic thought offers both universal and contextual poles – not just the latter, as that would be tantamount to moral relativism."


The dharmic approach lies in between an "all-soft constraint" and the Kantian "hard and soft constraint" approach to decision optimization.


Applying contextual optimization
Asimov's telepathic robot Giskard formulates and solves a probabilistic optimization problem where it trades off the opportunity cost (in terms of human lives) against the expected benefit to humanity. However the degree of uncertainty in this conflict-resolution model is too high and the robot eventually crashes. This episode comes across as an example of applying the CE approach to resolve a dilemma. The great Indian epics - the Ramayana and the Mahabharata, contain several brilliantly narrated instances of contextual conflict-resolution. Indian sci-fi movie buffs would not be surprised to know that George Lucas' Star Wars was inspired by the Ramayana.

Contextual optimization in the specific area of 'mathematical decision support software' would mean: allowing the rules of engagement to be configurable depending on the context. For regular users, advanced settings are greyed out, with only universal (default) rules enabled. Only super users, who are well-trained and comprehend the nature and consequences of the beast, get to work with 'firm' constraints, and on rare occasions. For example, an airline crew schedule optimization system should be configured to satisfy contractual and FAA rules, except during emergencies (e.g. post 9/11 recovery) where 'crew welfare' is only achievable by overriding one or more of these rules. Practical decision support systems should be carefully designed to allow such controlled contextual optimization.

Amending the Zeroth Law: The Dharmic Robot
The four laws do not quite protect the rest of the cosmos (e.g., from humanity) given their anthropocentric nature. From an Indian point of view, this gap can be closed by modifying the zeroth law based on the contextual ethics of dharma. Rajiv Malhotra, in his book, provides the etymology and a working definition of dharma:

"Dharma has the Sanskrit root dhri, which means 'that which upholds' or 'that without which nothing can stand' or 'that which maintains the stability and harmony of the universe'. Dharma encompasses the natural, innate behaviour of things, duty, law, ethics, virtue, etc. For example, the laws of physics describe current human understanding of the dharma of physical systems. Every entity in the cosmos has its particular dharma – from the electron, which has the dharma to move in a certain manner, to the clouds, galaxies, plants, insects, and of course, man. Dharma has no equivalent in the Western lexicon."

In such a framework, Asimov's laws would delineate a robot's various dharmas. At the highest level, we can require that a robot abide by the following fundamental law, that is based on an ancient Indian text:

"Non-harming is a robot's highest priority, except in the defense of dharma"

Conflict-resolution is always performed by first applying this highest dharmic principle and customizing it to the specific context. Note that by operating on the fundamental dharmic principle of least harm, a robot would usually satisfy Asimov's zeroth law, albeit in a context-specific manner, while also being in harmony with the original laws, as well as any new laws that get written in the future. Interestingly, the Hippocratic oath of medical doctors is based on a similar idea that represents a non-negative bound: "do good, or at least no harm".  If complex systems, new drugs, etc., are designed by always keeping this fundamental principle in context, it may well minimize the risk of catastrophic failure.

Collection of Notes on Gun Control

Notes update 1: Dec 18.
Notes update 2: Dec 19.
Notes update 3: Jan 02.

Introduction: The US Outlier
This post is merely an extension of the discussion initiated by Professor Rubin on how not to debate gun control - a discussion that is well worth reading, and one that I have referred to multiple times to try and comprehend a situation that is unique to the US while also sharing certain tragic commonalities with other countries. Much of this post are 'note to self' type remarks for future reference and is a largely unstructured collection of pointers to various articles and personal inferences drawn, which may change as more data becomes available.

The country-independent issues are brought out well in the blog cited in Prof. Rubin's post. That the issue of gun-ownership & deaths also has a US-specific dimension is apparent in this picture presented by a comment on Dr. Rubin's blog.

(link source: https://dl.dropbox.com/u/38668/deaths-vs-guns.png, Courtesy 'Christian')

Macro-Trends

Note however that Newtown was a specific incident (a 'sample of one', as Dr. Rubin notes), whereas this picture above captures macro-trends. Examine these six time-series based facts published by the Washington Post. Assuming this report is factual, the first graphic reconfirms the existence of a unique US situation shown in  the first scatter plot. We will focus our attention on just the first plot in this post. There are other interesting plots that show:

-  a correlation (not necessarily causality) between stricter gun-controlled US states and lower rates of gun-related deaths

- Southern US has the highest gun-driven fatality rates, while the Northeast has the lowest,

- Gun ownership rates are declining, etc.

The above graphic reveals that overall gun-related fatality rates in the US have monotonically dropped over the last twenty years. Neither the economic boom of the 1990s or the relative economic decline in the last decade, or demographic shifts, etc. has managed to cause a significant inflection at the macro-level. Does that mean that the violence in the society has dropped overall (reduction in market size), and/or have people found alternatives to guns to express their violence (reduction in market-share)? We may need several other charts to answer that. However, purely based on this time-series, it appears that what we have in place has apparently worked relatively well at a macro-level so far. We may be tempted to think that by maintaining the status-quo we are on course to achieve parity with the rest of the pack with respect to this specific metric, but to predict the future is difficult, and requires some knowledge of causality (or at least correlations that can give us a tiny hint).


Significant Incremental Improvement
To motivate the next discussion, let us recall this very useful comment by Dr. Rubin:
"Many (including, I'm willing to bet, Mr. Cassingham) would consider a reduction in mass shootings, or even a reduction in the body counts, as a significant improvement (satisficing)."

From the point of view of the person who wants to act violently, they may well use the first (or most suitable) 'weaponizable' object they can first lay their hands on, and thus a gun or a knife may well represent alternative optimal solutions to a perpetrator. The time durations for:
a. formation of violent intent
b. searching for optimal method to express intent
c. translating intent into reality
seem to be quite important. These time durations may range from a few milliseconds to years. Mr. Cassingham's blog rightly notes in more than one place (and I paraphrase) 'if not guns, then people will always find something else' (in stage (b). True. Addressing the root cause of violence (that arises in a person's mind, and incubated over varying time durations in stage (a)) is very important to the discussion, so that we never get to see the realization of later stages. Perhaps addressing each of these stages (a-c) in some order of priority may help.

Addressing (a) is likely to reduce the probability of future incidents, and is something that may have to be addressed in from the short-term p.o.v (e.g. using medical science), and all the way to the long term where the world can move beyond mere tolerance for differences to mutual respect that celebrates difference. Addressing (b) can reduce the expected consequence of a violent incident, given that a tragic incident does occur (ideally to zero). Addressing (c) appears to be tied to 'last line of defense' mechanisms in place.

One of the key contributions of Operations Research is to recognize alternative optimal solutions.  Dr. Rubin's idea of 'optimality versus 'satisficing' seems pretty valuable. From the point of view of expected casualties | given an attempt to mass-injure, a smaller gun, a knife or a baseball bat will probabilistically have a lower casualty rate. An explosive device or a WMD-type gun (like that used in Newtown) is likely to kill more per unit time, on average. Thus from the perspective of a group of trapped victims, horrific as it may be, there may exist a reasonably well-defined ranking of what weapons we least prefer to face (obviously 'facing no weapons' would be ideal). It would beneficial if those who already in stage (b) are always forced to switch to a weapon that is far less deadlier than what was available before. We could think of the deadliness of the weapons permitted as a time-variable 'upper bound' on weapons control. There may be a variety of such bounds along each dimension of the weapon and ammunition, as well as lower bounds on re-training frequency, life-event triggers for re-evaluation of permits, etc. Doing so may provide a gradient along which this discussion can take constructive steps toward reaching the most satisfactory solution by iteratively adjusting these bounds. Again, there are likely to be multiple goals having different priorities as already mentioned in Prof. Rubin's post. The presence of constraints may result in 'satisficing' rather than achieving the desired 'global optimality'. One also wonders about the disruptive impact of 3D printing of weapons on such discussions.




(pic linked from popular science)



What do we do when people have passed through stages (a)-(b), and ready to move to stage (c)? This is the stage, where their intent, as well as degree of planning starts to become visible to others, but the duration may be extremely limited...


Average versus Distribution
The next discussion borrows from a very recent post of another ORMS professor.  Dr. Trick wrote about the tricky issue of averages recently in his post "which average do you want?".  In addition to the amazing and heroic teachers and staff of Sandy Hook Elementary School, Newton, we just lost eighteen of our most precious and littlest citizens in a matter of minutes after near-complete peace and quiet in that area during the previous 364 days. Such sudden, scarcely comprehensible mass murders (low-probability, catastrophic consequence events) suck the oxygen out of a nation's morale compared to an alternative and equal cumulative tragedy that is well spread out over time and space. Dr. John Cook's blog today talks about the nonlinear effect of 'batch size'. In either of these equally tragic scenarios, from every individual victim's family p.o.v, the anguish and price paid over time is likely to be the same - too high to imagine.  Picture 1 of the WaPo article only reveals a reduction in the overall rate, but not the changes in the distribution (victim's age, motive, ..). The nation's response last week leaves little doubt that the distribution of this statistic is just as much if not more important.

Sam Harris notes in his "The Riddle of the gun" :
"... As my friend Steven Pinker demonstrates in his monumental study of human violence, The Better Angels of Our Nature, our perception of danger is easily distorted by rare events. Is gun violence increasing in the United States? No. But it certainly seems to be when one recalls recent atrocities in Newtown and Aurora. In fact, the overall rate of violent crime has fallen by 22 percent in the past decade (and 18 percent in the past five years).
We still have more guns and more gun violence than any other developed country, but the correlation between guns and violence in the United States is far from straightforward...

..... Seventy mass shootings have occurred in the U.S. since 1982, leaving 543 dead. These crimes were horrific, but 564,452 other homicides took place in the U.S. during the same period. Mass shootings scarcely represent 0.1 percent of all murders. When talking about the problem of guns in our society, it is easy to lose sight of the worst violence and to become fixated on symbols of violence ...  "

LPHC Events in the Life cycle of a Gun
Finally, to the gun itself. Like a pair of scissors, a kitchen knife, or a car, a gun is a tool that has associated with it a probability for helping and hurting simultaneously whenever it is is handled. Associated with each of these tools is an expected frequency of usage and a risk profile that is context dependent (such as geographical location). The intended target of the primary benefit is largely limited to self and family (who bear the cost of maintenance), whereas the cascading and probabilistic liability is necessarily borne by self, family, and others. Every additional gun, its type, and associated inventory of ammunition, increases this probabilistic liability to the owner, his/her family, and public in a certain way, while altering the expected probabilistic benefit to self in another way. In many instances, both the benefit as well as the liability of acquiring guns appear to be tied to Low-Probability, High-consequence events. In extremely peaceful places, the expected liability may exceed the expected benefit over the lifetime of the weapon, whereas in lawless places, the opposite may hold true.


Ahimsa: Notes from Books

Louis L'Amour noted:  "When guns are outlawed, only the outlaws have guns". One of his best books, where violence is almost a living character is 'The Daybreakers" that ultimately ends with a note on the self-realization of the main protagonist leading to his rejection of senseless violence: "We found our home, and we graze and work our acres, and since that day in the street of Mora when I killed Tom Sunday, I have never drawn a gun on any man. Nor will I ...".  King Asoka of India fought and won the bloody battle of Kalinga, but was so shocked by the carnage that he gave up further violent conquests and turned toward meditation and the Dharmic way of life. The multi-millennium old Sanskrit Shloka on Ahimsa (non-harming) in Hindu Philosophy says (ref: Hindupedia):

अहिंसा परमो धर्मः
धर्म हिंसा तथीव च


Translation: Non-harming is the ultimate Dharma (very loosely translated as 'righteous way of life'). Harming in service of Dharma (only) is equally virtuous. Consequently, enduring or ignoring, rather than opposing tyranny is not a virtue. The spirit of the second amendment (arguably) and Asimov's three (plus zeroth) laws of robotics come across as examples of the application of this profound Shloka. Some of Gandhi's more head-scratching ideas are apparently due to his ambivalent treatment of the second line of the Shloka, although the Mahatma did say (ref: http://www.mkgandhi.org)
"I do believe that, where there is only a choice between cowardice and violence, I would advise violence... I would rather have India resort to arms in order to defend her honour than that she should, in a cowardly manner, become or remain a helpless witness to her own dishonor. But I believe that nonviolence is infinitely superior to violence, forgiveness is more manly than punishment"

In Remembrance: Dr.L (2007) and Little Maddy (2012). Om Shanti.