Darwinism tells us that there is no such thing as a fair fight. All vulnerabilities must be exploited. It is survival of fittest. To the winner goes the spoils. Even business operates that way. The competition must be crushed. The salesperson of the month has vanquished other people in the department. In the corporate world, if you beat out your competition, you get the raises, promotions, perquisites and ego-stroking recognition. In Life, you get all of the toys and joys. But is that the ideal way to operate? Is competition the best personal, societal or business strategy?
As you may or may not know, my web scrapers and Natural Language Processing tools glean articles from the web and turn content into knowledge graphs. Lately an unexpected haul came from some web scrapings that enabled me to see living in the modern world in a different light.
Most of our daily existence is problem-solving. Where are we going to get money to live? How can we make the most money to survive? What jobs do we enjoy doing? What groceries must I buy today? What time should I go to bed? What is the most nutritious thing to eat? What needs immediate addressing today in my family, relationships, job, household, property, health or mental status?
If your life problems were computer science problems, it would fall into the category of optimization problems. In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Finding the best possible solution among a myriad of potential solution pathways is tough.
In most cases, you do not have the tools and information to solve problems yourself. That is why you need help. That’s why many people hire coaches. There are problems with the coach approach, because most coaching solutions are fairly abstract. They often don’t give you specifics on how to solve your problems.
One of the problems of coaches, is that anyone can be a coach by just saying that they are one. I’ve seen coaches who offer services to mentor Chief Technology Officers who have never been CTOs themselves. It like me and golf. I would never hire a golf coach that I could beat on the golf course. Some people argue that that is not valid. They tell me that even great golfers hire coaches who they can beat at golf. But when it comes to the pros like Tiger Woods, he hired the likes of swing coach Hank Haney. Haney can’t beat Tiger at golf, but he has a more consistent swing and drive than Tiger. Not longer, but more consistent. He is like a machine when it comes to swinging a driver. What the ideal coach should do, is impart knowledge gleaned from experience — by playing the game — whatever game they are coaching. But the unsaid elephant in the room is that most successful, great coaches have exceptionally gifted teams or mentees with extraordinary talents. You can’t make a silk purse out of a pig’s ear. That applies to performance coaching, but what about life coaches?
I did a search for “Life Coaches” on LinkedIn and four out of the first five results were attractive women in their 30’s without degrees in psychology or any other discernible tertiary degrees in related fields. The first one had a profile badge that advertised that she was “Open to Work”. The second one was nutritionist which isn’t exactly experience in advising in the career domain. You get the gist.
So if you are going to solve your problems, either as an individual, a team or a business, you need to examine metaheuristics. Metaheuristics are higher-level procedures or heuristics (problem-solving algorithms) designed to find, generate, tune, or select a partial search algorithm that may provide a sufficiently good solution to an optimization problem with incomplete or imperfect information or limited research capacity.
There are many metaheuristic algorithms. Some are Genetic Algorithms (trying different variations, variables and/or modifications in your main algorithm until one succeeds); Simulated Annealing (inspired by the annealing process in metallurgy when you iterate for an acceptable solution using different amounts of set variables in the main algorithm); Tabu Search (maintain a list of recently explored solutions so you “remember” which ones didn’t work; and the one that I feel is the best, because it is a crowd-searched, distributed, cooperative method to optimization solutions. It is the ACO or Ant Colony Optimization. It is the preferred algorithm of the most socially successful, productive and efficient inhabitants of Earth — the ants and the bees.
Here’s the way it works in an ant colony. All of the foraging ants spread out in a random manner. Some ants go it alone and some go in small groups. As they do their random walks they drop the odd scent molecule - a pheromone. It is sort of a trail marker for themselves and other ants. When they discover a food source, they rush back along the route dropping more pheromones. Other ants pick up on this and drop their own pheromones on the same route. Eventually by the effort of many individuals, the optimal solution is found by the pheromone density of each trail. But food sources are not infinite and/or stable. They get used up. The base problem isn’t fixed. It is dynamic and volatile. The way that it is handled, is that the sun evaporates the pheromone trails. They decline and either old trails are reinforced or new trails are created by the same process. Bees operate in a similar fashion of cooperation without pheromones. When a forager bee discovers a food solution, it rushes back to the hive and communicates the coordinates to her fellow workers with an intricate dance that they understand. Many hands (or feet in this case) makes light work.
In the human milieu, the basic requisite for an effective, distributed problem solution algorithm, is to have a collection of people — a tribe, a business, a department, a clan, a club or a group. Various solutions are brainstormed. This is the modeling phase. The initialization phase is when various members set out along solution paths proposed in the models. As each individual moves along each potential solution paths, they leave virtual pheromones like progress reports, milestones achieved or fastest time. Ants tend to favor routes with higher pheromone levels, shorter distances, quicker times and higher yields. This process continues until you see your way clear to solution construction and final algorithm solution. Then comes the pheromone update phase. All pheromones degrade and require updates. Those most successful heuristics or solutions get the most updates which results in a convergence to the solution of the optimization problem.
Did you see what happened? There was no competition among the members of the group. They all contributed to the success of the organization as a whole. In most businesses, the salesman of the month keeps their knowledge secret from their colleagues. That isn’t exactly good for the business. In a dog-eat-dog world out there, there isn’t a successful pack in that paradigm — just an eventually fat dog.
In a more personal aspect, several success mavens through the years have proposed setting up a personal Mastermind Group to help you find your way in the world. This would be friends, colleagues, neighbours or family. This is/was one of the functions of fraternal societies like the Masonic Orders. The group as a whole prospers and brings everyone along.
The biggest winners of the Ant Colony Optimization algorithms are obvious. They are corporations, businesses, political parties, governments and activist groups. With everyone cooperating instead of competing in problem-solving, this world may be a kinder, gentler place with a healthier society where no one is left behind. This isn’t what brought us here. It was Darwinism, but it may enable us to move beyond the nasty brutish world that many of us experience.
Thanks for reading.