Why swarm intelligence enhances business and Bitcoin

Peter Nichol
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Why swarm intelligence enhances business and Bitcoin

Can intelligence be amplified by thinking together? Ants do it. Birds do it. What about humans? Collective intelligence is the next wave of intelligence. Swarm intelligence connects systems with real-time feedback loops. Individual efforts combine to form a greater value.

Fish school. Birds flock. Bees swarm. A combination of real-time, biological systems blends knowledge, wisdom, opinions and intuition to unify intelligence. There’s no central control unit. These simple agents interact locally, within their environment, and new behaviors emerge.

Swarm intelligence is the self-organization of systems for collective decentralized behavior. Swarm intelligence enables groups to converge and create an independent organism that can do things that individuals can’t do on their own.

Why can’t humans swarm? Fish detect ripples in the water. Birds use motion detected through the flock. Ants leverage chemical traces. Until recently, there’s been little research conducted on “human swarming.” If nature can work together, why can’t humans use similar decision spaces to arrive at a preferred solution? Will the next generation of breakthrough innovation stem from the wisdom of the crowd — swarm intelligence?

Whether we’re talking about nature, humans or robots, swarm intelligence creates a virtual platform to enable distributed engagement from system users. Through this engagement, feedback can be provided in a closed-loop, swarming process.

Individual force for unified objectives

Swarm intelligence draws from biologically inspired algorithms to enhance robotics and mechatronics. Evolutionary optimization is more than ant-colony optimization algorithms (ACO), bee-colony optimization algorithms (BCO) or particle-swarm optimization (PSO). Swarm intelligence can be applied to immune systems, computer vision, navigation, mapping, image processing, artificial neural networks and robotic motion planning.

Bio-inspired systems bring new intelligence to the design of robotics and are used in aerial flying robots, robotic manipulators and underwater vehicles.

The physical, biological and digital worlds benefit immensely by learning from nature. These bio-inspired applications are creating swarm algorithms empowering a newly discovered digital autonomy.

Ants and distributed systems

Technology-based distributed systems are collections of independent computers that appear to work as a unified, coherent system. This same effect is found in swarms. The common element is that control is distributed across individuals or entities and communication isn’t localized.

Why is Bitcoin so fascinating to us? Could it be that the Bitcoin network is a self-organizing, collective intelligence similar to that mesmerizing school of fish?

The collective intelligence, or COIN, framework was first introduced in a paperpublished in 2000 by John Lawson and David Wolpert of NASA’s Ames Research Center.

This framework helped identify — using similar system attributes — where collective intelligence might exist.

  1. Multi-agent system.
  2. No central operator.
  3. No centralized communication.
  4. Unified utility function.
  5. Agents run reinforcement learning algorithms for validation.

Bitcoin is a large version of a multi-agent, reinforcement learning system. The same challenge injected into swarms is inherent in Bitcoin: How are rewards to individuals, agents or entities assigned? The social aspects of swarms are both simple and complex. Group behavior emerges as more significant than individual actions — complexity out of simplicity.

Swarms can solve more than just static problems. Units interact in localized ways and can solve online, offline, stationary, time-varying, centralized, distributed and dynamic problems.

How does a swarm live? How does a swarm communicate? A unique “life” takes shape when a swarm forms, and it has everything to do with spatial intelligence. When observing swarms, we start to notice certain principles:

  1. Work division
  2. Collective behaviors
  3. Navigation
  4. Communication
  5. Self-organization

Social survival

Dinosaurs weren’t social. Ants are social, and they have outlasted dinosaurs and are able to survive in a range of environments and climates. How do ants build their nests? How do ants navigate? Why can ants locate food fast? There’s a one-word explanation: sociality.

The key to human survival isn’t having sophisticated intelligent robots that will floss your teeth while you’re in the shower. The secret is sociality. We must build social systems when we design intelligent systems. There are many examples of nature’s social systems we can draw from:

  • An implausibility of wildebeest: They move through rivers in sheer numbers to avoid crocodiles.
  • A rabble of butterflies: Monarch butterflies migrate to escape the cold North American winters.
  • A rookery of penguins: Emperor penguins converge in a huddle to stay protected from the Antarctic winters.
  • A business of mayflies: Use swarms of 8,000 to attack predators in volume.
  • A plague of locusts: Synchronize their wing beats to make travel more efficient.
  • A shoal of fish: Silver carp leap into the air as a unit to avoid predators.
  • A pod of dolphins: Superpods of dolphins, which can exceed 1,000 individuals, form a pod for protection and hunting.
  • A flight of birds: Budgerigars, a type of parakeet, assemble to act as a unit to make decisions, fend off predator attacks and find food.
  • A cloud of bats: a social vortex of bats forms for communication and to make decisions on foraging.

Nature’s progression and technology’s evolution are amplified with social systems. The end of social abnormalities may be the introduction of swarm intelligence.

Is there a better way to build super-intelligence?

Let’s collect lessons from nature, insights from humans and the unified benefits of intelligent systems and create something smarter than ourselves. These intelligent systems — things smarter than ourselves — appear to think and act. The algorithms, robotics and systems are only a piece of the system we’ll create. Instead of creating and designing complete intelligence systems, maybe we should apply simple rules to form collections of behaviors or swarms.

These swarms could respond by connecting real-time human insights into more intelligent systems with morals, values, emotions and empathy. Swarm intelligence won’t be something you watch on a Ted Talk. Swarm intelligence is going to be a feeling that transcends nature through a collision of the digital and physical worlds.

Tomorrow’s systems will be designed with swarm intelligence and spatial judgment.

Peter B. Nichol, empowers organizations to think different for different results. You can follow Peter on Twitter or his personal blog Leaders Need Pancakes or CIO.com. Peter can be reached at pnichol [dot] spamarrest.com.

Peter is the author of Learning Intelligence: Expand Thinking. Absorb Alternative. Unlock Possibilities (2017), which Marshall Goldsmith, author of the New York Times No. 1 bestseller Triggers, calls “a must-read for any leader wanting to compete in the innovation-powered landscape of today.”

Peter also authored The Power of Blockchain for Healthcare: How Blockchain Will Ignite The Future of Healthcare (2017), the first book to explore the vast opportunities for blockchain to transform the patient experience.

What do cognitive science and swarm intelligence have in common?

Peter Nichol
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What do cognitive science and swarm intelligence have in common?

The future of artificial intelligence is self-organizing software. Multi-agent coordination and stigmergy will be useful in our quest to discover dynamic environments with decentralized intelligence.

In every field, there’s a pioneer, a prototype, an individual or group that blazed the path forward to uncover previously hidden value. Observing the giants in artificial intelligence allows us to revisit the early instrumental concepts in the development and maturation of the field. Biological principles are the roots of swarm intelligence, and self-organizing collective behavior is its organizing principle. Better understanding these foundational principles results in the ability to accelerate the development of your business applications.

The movers and shakers of artificial intelligence

Four pioneers shaped artificial intelligence as we know it today.

Allen Newell was a researcher in computer science and cognitive psychology at the RAND Corporation and Carnegie Mellon University’s School of Computer Science. His primary contributions to information processing, in collaboration with Herbert A. Simon, were the development of the two early A.I. programs: the Logic Theory Machine (1956) and the General Problem Solver (1957).

Herb Simon was an economist, sociologist, psychologist and computer scientist with specialties in cognitive psychology and cognitive science, among many other fields. He coined the terms bounded rationality and satisficing. Bounded rationality is the idea that when individuals make decisions, their rationality is limited by the tractability of the decision problem, the cognitive limitations of their minds and the time available to make the decision. Satisficing (as opposed to maximizing or optimizing) is a decision-making strategy or cognitive heuristic that entails searching through the available alternatives until an acceptability threshold is met. Simon also proposed the concept of the preferential attachment process, in which, typically, some form of wealth or credit is distributed among individuals or objects according to how much they already have, so that those who are already wealthy receive more than those who are not.

John McCarthy was a computer science and cognitive scientist who coined the term artificial intelligence. His development of the LISP programming language family, which heavily influenced ALGOL, an early set of a programming language developed in the mid-1950s, emphasized the value of timesharing. Timesharing today is more commonly known as multiprogramming or multitasking, where multiple users share computing resources. McCarthy envisioned this interaction in the 1950s, which is nothing short of unbelievable.

Marvin Minsky, a cognitive scientist, was the co-founder of MIT’s artificial intelligence laboratory. In 1963, Minsky invented the head-mounted graphical display that’s widely used today by aviators, gamers, engineers and doctors. He also invented the confocal microscope, an early version of the modern laser scanning microscope.

Together these framers laid the foundation for artificial intelligence as we know it today.

The design for mass collaboration

Do we understand collaboration? Thanks to Kurt Lewin and his research on group dynamics, we understand how groups interact much better than we thought. I ask again, do we understand group interactions? Is there an ideal group size? What’s the best balance of independence? Is the group interaction better or worse when we design in patterns for group activities?

We have defined paradigms of productive and unproductive group interactions. Our challenge comes from the fact that these models don’t scale. It’s also the same reason that the suggested agile team size is seven people plus or minus two team members. As group size increases, so does the complexity in the lines of communication. A team of six people has 15 lines of communication, a team of seven people has 21, and a team of nine people has 36 lines of communication [n members in a group produces n(n-1)/2 lines of communication]. Yet, in spite of the problem of the complexity in lines of communication, colonies of ants reaching 306 million workers interact fine as does a mayfly swarm of 8,000 flies. Both groups organized around common goals.

How is this possible if this line of communication principle is absolute? To state it simply, it’s not absolute. We can change the lines of communication by adjusting how the group interacts. This same concept can be applied for swarms of drones and self-organizing software. The limit that prevents us logically from adding agents due to communication complexity — a system we as innovators can simply redesign — is defined by our communications systems.

Psychologist Norman Triplett concluded that bicyclists performed better when riding with others. He found a similar result in the study of children: pairs performed better than solo actors.

Lewin, Lippitt and White later studied what happened to the behavior of young boys (10-11 years old) when an adult male joined the group. The group adopted one of three behavior styles, which the authors named autocratic, democratic and laissez-faire. The results were surprising. The autocratic style worked when the leader merely observed the boys’ behavior. The democratic style worked when the leader wasn’t present with the team. The laissez-faire style was found to be least effective. Does democratic mass collaboration result when the leader is absent?

Group dynamics of biology and computer science

Sociometry is the quantitative study and measurement of relationships within a group of people. Does sociometry apply to swarm interactions?

A swarm is simply a group, right? What if we could design intelligence systems to optimize learning? These systems wouldn’t only exemplify stigmergic environmental properties. They would also build on properties of traditional group dynamics. If you’re in the gym and notice people are staring at you, you’re able to bike a little harder, run a little faster, or lift a little more. What if we could design artificial intelligence systems that would be intelligent enough to embrace these same feelings? Sure, we’re talking less about feeling and more about procedures or rules that we apply in context — but the term “feelings” sounds better to me.

Collective behaviors contribute to solving various complex tasks. These four principles are found in insects that collectively organize. They should also be found in the artificial intelligence systems we create:

  1. Coordination: organizing using time and space to solve a specific problem.
  2. Cooperation: agents or individuals achieve a task that couldn’t be done individually.
  3. Deliberation: multiple mechanisms when a colony or team faces multiple opportunities.
  4. Collaboration: different activities performed simultaneously by individuals or groups.

Whether we’re adding blocks to a blockchain or changing the rights individuals have to shared content, the study of interactions might hold the key to unlock the next generation of artificial intelligence. Before exploring the benefits of dynamic systems and chaos theory, we must apply the principles of artificial intelligence, mass collaboration and group dynamics to expand our knowledge of how systems self-organize.

Peter B. Nichol, empowers organizations to think different for different results. You can follow Peter on Twitter or his personal blog Leaders Need Pancakes or CIO.com. Peter can be reached at pnichol [dot] spamarrest.com.

Peter is the author of Learning Intelligence: Expand Thinking. Absorb Alternative. Unlock Possibilities (2017), which Marshall Goldsmith, author of the New York Times No. 1 bestseller Triggers, calls “a must-read for any leader wanting to compete in the innovation-powered landscape of today.”

Peter also authored The Power of Blockchain for Healthcare: How Blockchain Will Ignite The Future of Healthcare (2017), the first book to explore the vast opportunities for blockchain to transform the patient experience.

When will science create ethical robots?

Peter Nichol
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When will science create ethical robots?

Is it possible for robots to have a bias? If robots attempt to imitate human behavior, isn’t it possible that they could make unethical choices — just as people can make unethical choices?

Your bed isn’t able to move between rooms automatically with only a wireless phone request. Your toaster can’t make a bottle of water. Your garage door doesn’t wash itself. The bed, the toaster and the garage door each perform a specific function well — the function we need — nothing more nothing less.

But, what if on Monday your bed sensed that you should be at the gym at 9 a.m. and vibrated to force you to get up. Then the toaster didn’t turn on because it decided that you didn’t need those extra carbs in the bagel. It was helping you. And maybe you had been doing a lot of traveling, and the garage door knew that the ergonomics of traveling too much would be bad for your spine, so it didn’t open when you got in your car. Welcome to the intelligence world of smart A.I.

Can A.I. machines, agents and robots be too smart? Just because we could design a machine to be intelligent, doesn’t mean that we should.

Robots attempt to imitate human behavior. Then isn’t it logical that if ethical people can make unethical choices, that ethical robots could make unethical choices?

The moral compass of machines

Humans have morality. These guiding principles help us make the distinction between right and wrong or good and bad behavior. This concept centers around ethics, the philosophy to examine right and wrong moral behavior with ideas such as justice, virtue or duty.

When we think about our car, we might be interested in fuel economy. On reflections of our health, topics like comfort and lifestyle come to mind. And when our thoughts migrate to nature, we may think about natural selection and survival of the fittest.

The pontification of morality and virtue lands us quickly in the world of consequentialism. This doctrine holds that the morality of an action is to be judged solely by its consequences. The actions can have multiple and conflicting outcomes. If we as humans have trouble making these decisions, how are we going to program machines to make them? Utilitarianism could be a solution. We have more than one choice when deciding how we design machine intelligence.

  • Consequentialism: helps determine whether an act is morally right only based on consequences.
  • Actual consequentialism: adds that moral rightness depends on the actual consequences.
  • Direct consequentialism: assesses whether the act is moral based on the act itself.
  • Evaluative consequentialism: shifts the morality to the value of the consequences.
  • Hedonism: an entertaining derivative of action, determines moral rightness based on pleasures and pains of the consequences.
  • Maximizing consequentialism: depends on which of the consequences are best (versus average).
  • Aggregative consequentialism: focuses on moral rightness within function of the values of the parts of those consequences.
  • Total consequentialism: assesses moral rightness based on the total or net good of the consequences.
  • Universal consequentialism: is the assessment of moral rightness for all people involved in the consequences.
  • Equal consideration: determines moral rightness based on an equality of the consequences among the parties involved.
  • Agent-neutrality: moral rightness does not depend on whether the consequences are evaluated from the perspective of the agent or observer; it gives every agent the aim of maximizing utility.

Let’s just quickly program morality into the machine and get on our way. It turns out that programming morality is complex, even before we get to the evaluation of outcomes experienced through machine intelligence or robotic involvement.

Linking machine intelligence to ethical philosophy

Roboethics, or robot ethics, is how we as human beings design, construct and interact with artificially intelligent beings. Roboethics can be loosely categorized into three main areas:

  1. Surveillance: the abiSurveillance: the ability to sense, process and record; access; direct surveillance; sensors and processors; magnified capacity to observe; security, voyeurism and marketing.
  2. Access: new points of access; entrance into previously protected spaces; access information about space (physical, digital, virtual); objects in rooms, not files in a computer, e.g. micro-drones the size of a fly.
  3. Social: new social meaning from interactions with robots that implicate privacy flows; changing the sensation of being observed or evaluated.

Robots do not understand embarrassment. They don’t have fear, and they are tireless and have perfect memories. Designing robots that spy, either on your back porch or while your car is parked, brings into question how surveillance, access and social ethical considerations will be addressed as we further develop algorithms that assist humans.

We’ve heard about machine intelligence agents to enable ubiquitous wireless access to charge our mobile phones autonomously. We’ve fantasized about eating pancakes in bed while robots serve us (or maybe that was just me). There have been a lot of technological advances since George Orwell’s 1984 ramblings about the risk of visible drones patrolling cities. Or we could just reject the Big Brother theory altogether and join the vision of Daniel Solove, where we live in an uncertain world where we don’t know if the information collected is helping or hurting us.

The First Amendment appears like a logical addition. But how do we balance excessive surveillance with progress without violating the First Amendment’s prohibition on the interference with speech and assembly?

As we answer a question, three more rise to the surface.

Where is machine learning being used?

How much sensitivity do we design into machine intelligent beings? How much feeling should we architect into an armed drone? Should the ethical boundaries change if we’re simply designing a robotic vacuum cleaner that could climb walls? Where do we make the line between morality and objectives? You better cook my toast today. But tomorrow, I’m OK if the refrigerator is locked shut because I have exceeded my caloric intake for the day.

Society, ethics and technology will experience the heavy integration of rights and moral divisions over the next 10 years. Who designs the rules, processes and procedures for autonomous agents? This question remains unanswered.

Peter B. Nichol, empowers organizations to think different for different results. You can follow Peter on Twitter or his personal blog Leaders Need Pancakes or CIO.com. Peter can be reached at pnichol [dot] spamarrest.com.

Peter is the author of Learning Intelligence: Expand Thinking. Absorb Alternative. Unlock Possibilities (2017), which Marshall Goldsmith, author of the New York Times No. 1 bestseller Triggers, calls “a must-read for any leader wanting to compete in the innovation-powered landscape of today.”

Peter also authored The Power of Blockchain for Healthcare: How Blockchain Will Ignite The Future of Healthcare (2017), the first book to explore the vast opportunities for blockchain to transform the patient experience.

Why neural networks and deep learning hold the secret to your health

Peter Nichol
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Why neural networks and deep learning hold the secret to your health

Your daily habits could be interrupted by connected systems enabling access to new processing paradigms. Information processing systems inspired by biological nervous systems may change your diagnosis.

We’re supposed to eat less, work out more and use less salt. These goals rarely materialize into a productive pattern. Our behavior doesn’t change. Even with the incredible amount of information available, we choose not to change.

Artificial neural networks (ANN) have the ability to influence medical diagnoses and change our behavior. Change is more than what you should or shouldn’t do. How you connect data and squeeze out information also impacts our ability to change.

Artificial neural networks forecasting our health

Artificial neural networks have a wide range of uses in science and technology with applications across chemistry, physics and biology. The simulation of neural networks has been used to enhance group tactics for playing soccer, fighting crime, accelerating facial image processing and expanding nanotechnology.

Artificial neural networks can address nonlinear problems by mapping multidimensional data sets into two-dimensional spaces. They “learn” based on input and outputs as information flows through the network. The flow of information changes the structure of the artificial neural network. These networks evolve independently.

Classifications of neural networks

Connecting things adds value. The TV doesn’t do much good without power. It’s helpful to know your iPhone data is backed up with cloud storage. Spending money can get you fed at a restaurant.

Usually, we think of combining physical things to create more value. However, the greatest value gains have nothing to do with physical objects; they have everything to do with combining data to form new information. This information has value and forms the utility of artificial neural networks. They combine data into information we’d otherwise never have created.

The key to understanding artificial neural networks begins by identifying the types of artificial neural network we’re talking about. There are four main classifications of neural networks, within a field where over 50 types exist.

  1. Dynamic neural network: networks that either form or do not form a cycle.
  2. Static neural network: networks with no context memory.
  3. Memory network: networks with context memory.
  4. Other types of networks: networks that operate similar to neuronal (mathematical functions) and synaptic states (linking neurons) with the additional feature that these networks also incorporate the concept of time into their operating model.

The challenges of scientists

Scientists and technologists have long had an interest in neural networks. Cognitive science, parallel processing, control theory, neurophysiology, physics, artificial intelligence and computer science all must merge to form the base of knowledge to design, construct and implement artificial neural networks. This field challenges scientists to address the following problems:

  1. Pattern classification: divide the items into categories and then identify those patterns.
  2. Clustering and categorization: unsupervised pattern classification with no training, by identification of similar patterns.
  3. Functional approximation: the function is tasked to find an estimate or the unknown value through various engineering and modeling techniques.
  4. Prediction and forecasting: uses a time sequence data set predict a sample to help make decisions typically in business or science.
  5. Optimization: identifies a solution given a set of constraints for problems in science, medicine or economics.
  6. Content-addressable memory: the address of memory is the same or separate from the contents and content in memory and can be recalled even by partial input to show context.
  7. Control: the model generates a control input, so the system follows a desired trajectory based on a reference model.

Let’s make these problems more practical. Pattern classification could be used to identify abnormal EEG wave forms or for character recognition, speed recognition or blood cell classification. Clustering and categorization could identify high versus low risk populations based on blood or DNA samples. Functional approximation may help a patient decide to either have surgery or explore nonsurgical options (decision support). Prediction and forecasting, using nonlinear regression computational techniques, could aid in new drug discoveries, identification of regenerative medicine, or determine what effect that shake of salt will have on your lifespan.

Optimization can be used to minimize an objective function, e.g. swarm intelligence or robots working in a team for uniform interactions or movement. Content-addressable memory could be used for vision and pattern recognition, in combination with a learning algorithm to identify how viruses may mutate, helping the early discovery of cures. Control could be used to correct motion control problems in industrial applications, vehicles and surgical robots.

Connectionist models of survival

These models are especially applicable when predicting survival after the diagnosis of a rare disease. Would it matter if you knew you had 20 years left and not two months? It would matter to me.

It’s science that transcends from the cliff of innovation to the plateau of practicality.

Using artificial neural networks, combined with machine learning, medicine survival analysis can be calculated. Diagnosis, treatment-response forecasting and outcome predictions are just a few of the capabilities neural networks have demonstrated. Artificial neural networks may soon help change your behavior.

Peter B. Nichol, empowers organizations to think different for different results. You can follow Peter on Twitter or his personal blog Leaders Need Pancakes or CIO.com. Peter can be reached at pnichol [dot] spamarrest.com.

Peter is the author of Learning Intelligence: Expand Thinking. Absorb Alternative. Unlock Possibilities (2017), which Marshall Goldsmith, author of the New York Times No. 1 bestseller Triggers, calls “a must-read for any leader wanting to compete in the innovation-powered landscape of today.”

Peter also authored The Power of Blockchain for Healthcare: How Blockchain Will Ignite The Future of Healthcare (2017), the first book to explore the vast opportunities for blockchain to transform the patient experience.

Recharge your knowledge of the modern data warehouse

Peter Nichol
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0
Recharge your knowledge of the modern data warehouse

Data warehousing is evolving from centralized repositories to logical data warehouses leveraging data virtualization and distributed processing. Make sure you’re not using old terminology to explain new initiatives.

Are you comfortable with source systems feeding ETL processes into operational data stores or master reference data through an enterprise service bus with the product, supply chain and business operational reports dumped into a presentation layer with soft analytics, dashboards, alerts and scorecards? That was yesterday.

Don’t get caught, explaining your new data warehouse initiative with old terminology.

Traditional vs. modern data warehouses

Data warehouses are not designed for transaction processing. Modern data warehouses are structured for analysis. In data architecture Version 1.0, a traditional transactional database was funneled into a database that was provided to sales. In data architecture Version 1.1, a second analytical database was added before data went to sales, with massively parallel processing and a shared-nothing architecture. The challenge was that this resulted in slow writes and fast reads. In data architecture Version 2.0, the transactional database populated a second database which flowed into a third analytical database, which connected to the presentation layer (business intelligence). In data architecture Version 2.1, multiple transactional databases fed the core database which provided information downstream to data stores (sales, marketing, finance) that connected to a business intelligence engine. At this point, traditional database structures end and modern structures begin: data architecture Version 3.0.

The two below examples highlight the difference between a traditional data warehouse and a data a modern data warehouse (using Hadoop for this example).

Traditional data warehouse:

  1. Operational systems: CRM, ERM, financial, billing.
  2. ETL: decision analysis model and data.
  3. Enterprise data warehouse: operational, customers and IT data marts.
  4. BI platform: KPI summary, monthly and quarterly reporting and daily summaries.
  5. Automatic customer value analysis: interactive data queries, static data analysis, and OLAP.
  6. BI collaboration portal: wholesale, OEM, sales, employees and external.

Modern data warehouse:

  1. HDFS: Hadoop Distributed File System.
  2. HCatalog: a metadata and table and storage management layer system.
  3. HBase: key-value database, columnar storage.
  4. MapReduce: a flexible parallel data processing framework for large data sets.
  5. Oozie: A MapReduce job scheduler.
  6. ZooKeeper: distributed hierarchical key-value store enabling synchronization across a cluster.
  7. Hadoop: open-source software framework to support data-intensive distributed applications (storage, processing of big data sets).
  8. Hive: A high-level language built on top of MapReduce for analyzing large data sets.
  9. Pig: Enables the analysis of large data sets using Pig Latin. Pig Latin is a high-level language compiled into MapReduce for parallel data processing.

Most database designs cover four functions: 1) data sources, 2) infrastructure, 3) applications and 4) analytics. This principle of design does apply to both traditional data warehouses and modern architectures. The design thinking, however, is different. In a modern data warehouse, there are four core functions: 1) object storage, 2) table storage, 3) computation and processing, and 4) programming languages.

Re-establish the structure for success

The lack of data governance, inadequately trained staff, weak security and non-existent business cases each factor into why data warehouse or business intelligence initiatives fail to achieve the desired outcomes. Keep your data warehouse program on track.

Start by strengthening your framework for business intelligence. If it’s been more than six months since you looked at your end-to-end operational state, it’s a good idea to revisit the original thinking and revalidate assumptions.

The three-tier structure outlined here can help guide your discussions and the assessment.

Primary functions

  1. Program management: portfolio, process, quality, change and services management.
  2. Business requirements: business management, information services, communities, capabilities, service levels and value.
  3. Development: data warehouse and database, services, data integration, systems, monitoring systems and business analytics.
  4. Business value: culture; data quality, analytics and data utilization; data evolution; and value measurements.

Secondary functions

  1. Intelligence architecture: data, integration, information, technology and organizational.
  2. Data governance: accountabilities, roles, people, processes, resources and outcomes
  3. Operations: data center operations (SaaS, DaaS, PaaS, IaaS), technology operations, application support and services delivery
  4. Intelligence applications: strategic intelligence, customer intelligence, financial intelligence, risk intelligence, operations intelligence and workforce intelligence

Tertiary functions

  1. Data integration: data consolidation, data quality and master data management.
  2. Informational resources: data management, informational access and metadata management.
  3. Informational delivery: query and reporting, monitoring and management, and business analytics.

Shrinking budgets, pressure to deliver and expanding data sources all encourage us as CIOs to accelerate progress. Much of this acceleration comes at the cost of not thinking. The modern data warehouse is being designed differently. This means we as leaders need a block of time to think. This time also allows us to upgrade our understanding of how modern data warehouses are planned, refresh the core elements of the progressive data ecosystem and upgrade our terminology.

Evaluating your current data warehouse initiative

Start by asking the following questions to determine if you’re running a modern data warehouse.

  1. Does our environment quickly handle diverse data sources and a variety of subject areas?
  2. Can we handle excessive volumes of data (social, sensor, transactional, operational, analytical)?
  3. Are we using structures such as data lakes, Hadoop and NoSQL databases, or are we running relational data mart structures?
  4. Do we support a multiplatform architecture to maximize scalability and performance?
  5. Do we utilize Lambda architecture (more about data processing than data storage) for near real-time analysis of high-velocity data?
  6. Have we leveraged new capabilities like data virtualization (cloud services) in additional to data integration?
  7. Has the organization applied data warehouse automated orchestration for improved agility, consistency and speed through the release life cycle?
  8. Is our organization running a bimodal business intelligence environment?
  9. If we asked our primary business sponsors, would they know where the data catalog is located to document business terminology?
  10. Are the BI development tools decoupled from the agile deployment models?
  11. Have we clearly defined how we certify enterprise BI and analytical environments?

Modern data warehouses are comprised of multiple platforms impervious to users. Polyglot persistence encourages the most suitable data storage technology based on your data. This “best-fit engineering” aligns multi-structure data into data lakes and considers NoSQL solutions for XML or JSON formats. Pursuing a polyglot persistence data strategy benefits from virtualization and takes advantage of the diverse infrastructure.

If you’re well into the modern data warehouse journey but have not seen the benefits initially forecasted, don’t fear, there is still hope. Allow me to share a few tips to uncover the underlying challenges preventing successful adoption. First, define all the data storage and compression formats in use today. There are many options, and each one offers benefits depending on the type of applications your organization is running. Second, look at the degree of multi-tenacy supported in your BI environment. Using a single instance of software to serve multiple customers improves cost savings, makes upgrades easy and simplifies customizations. Third, review the schema or schema-less nature of your databases and the data you’re storing. Understanding how data is loaded, processed and analyzed can help to determine how to optimize the schemas of objects stored in systems. Fourth, metadata management, while often overlooked, can be almost more important as the data itself.

Upgrading your team’s understanding of data warehouses will move your organization toward agile deliveries, measured in weeks not months. 

The quiet revolution: the internet of data structures with IPFS

Peter Nichol
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The quiet revolution: the internet of data structures with IPFS

Everything of value involves data. Where data lives and how it’s accessed is about to change. The internet of data structures (IoDS) is transforming the web from linking data by location to linking data with hashes. Is your organization prepared?

The internet of data structures (IoDS) is emerging as one of the most significant advancements in data within the last decade.

HTTP (the hypertext transfer protocol) is the foundation communication the World Wide Web. Hypertext is structured text that enables us to access content throughout the web, using logical links (hyperlinks) between nodes containing data. But what if HTTP was no longer needed? What if there was a better way to communicate and connect data. IPFS (interplanetary file system) is a globally distributed storage system. Content is addressable and shared through a peer-to-peer hypermedia distribution protocol.

URLs are out; hashes are in.

The movement from HTTP to IPFS

Previously, I described the IPFS storage model and the benefits for healthcare. Today, we’ll step a layer deeper into how the structure is designed and I’ll offer an introduction to the IPFS stack.

Where does IPFS fit into our existing infrastructure? How do you communicate the value of this technology and the application of business transformation? Those questions are exactly what we’ll be tackling.

HTTP uses hyperlinks that translate into locations to connect discrete objects and data sets. IPFS is like HTTP, but instead of using locations provided by a group of servers, IPFS uses a peer-to-peer network to share context using hash values or hashes. In IPFS, content is addressable using hashes, the hashed value of the content.

IPFS is a Merkle addressed transport protocol for distributed data structures. The IPFS stack breaks down into three general buckets, each offering particular value.

  1. Using the data: applications (the IPFS stack)
  2. Defining the data: naming, Merkle-DAG (IPNS, IPLD)
  3. Moving the data: exchanges, routing, network (Libp2p)

These three primary buckets further divide into five broad categories that compose the infrastructure stack.

  1. Applications: Etherpad, VLC, Git, Ethereum, Whisper
  2. Naming: DNS, IPNS, EthNames, Namecoin or IPLD
  3. Exchange: BitTorrent, Bitswap, FTP, HTTP
  4. Routing: Gossip, Chord, Kad DHT, mDNS, Delegated, I2P, TOR
  5. Network: CJDNS, UDT, uTP, WebRTC, QUIC, TCP, WebSockets, I2P, TOR

Accessing files on IPFS

It’s easier to understand IPFS if we frame it next to concepts we’re already familiar with, like DNS.

The below HTTP example shows a typical website URL for a company logo and the host name translated into an IP address using DNS. Next, the IPFS example offers a comparative example using the IPNS (interplanetary naming system) and IPFS working together. IPNS allows the storage of a reference to an IPFS hash under the namespace of your peerID (the hash of your public key). This IPFS hash references an addressable object in IPFS, using a hash value that points to a hash object linked to another hash object until your destination is found.

IPFS also achieves immutability by separating key management from file system security. The filenames contain public keys making them self-certifying pathnames. Public key hashes, resolve pointers that are signed with a private key to access content.

HTTP

  • http://peterbnichol.com/linktohash/logo.jpeg (domain name service)
  • http://10.11.12.13/linktohash/logo.jpeg (IP address)

IPFS

  • /ipns/ReE45fRer5LR3/linktohash/logo.jpeg (InterPlanetary name service – optional)
  • /ipfs/ReE78kGrd5KJ2/linktohash/logo.jpeg (hash address)

The process of linking by objects is similar to how inodes operate, except using hash values. An inode is a data structure on a file system that stores all the information about a file except its name and its actual data.

Posting content with IPFS

IPFS offers a unique approach to addressing and moving content within a network. If other peers were uninterested in your content, then the standard paid backup solutions (AWS, Azure, Swam) could be leveraged. Also, unlike other peer-to-peer distributed networks, IPFS only downloads explicitly required data. IPFS does not pull full copies of data.

Publishing content to IPFS is similar to publishing content through a private blockchain. It’s also possible to distribute content on IPFS and then remove yourself as a host who serves that content (remove the need for infrastructure?). Here is an example of posting data in an IPFS world:

  1. Create content
  2. Generate key names
  3. Sign content
  4. Distribute to peer-to-peer network
  5. Register key name and point to hash of public key

In theory, this process removes the need for locally owned and managed infrastructure. In practice, standard paid backup services may be required such as those listed above.

What will be impacted?

Any products, services or interactions that leverage storage or save data have the potential to be affected. This foundational technology layer will affect where data is stored (traditional databases to IPFS) and how data is accessed (URLs to hashes). Every platform that requires linked and encrypted communications has the potential to benefit from IPFS. Dapps and mobile applications will quietly shift to internet of data structures as scale and interoperability become increasingly critical.

Distributed denial-of-service (DDoS) attacks would be harder to execute on platforms running IPFS. HTTP routes static traffic to a central server, this decreases the attack surface, making targeted attacks more efficient. However, IPFS is a distributed storage system. By using IPFS and distributing the attack surface across peers, this makes conducting DDoS attacks significantly more difficult because content can be accessed through the distributed storage network.

Innovative leaders are learning about IPFS: what it is, where it impacts the organization and how it can be used to create a strategy to leverage this foundational technology.

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A look at India’s biometrics identification system: digital APIs for a connected world

Peter Nichol
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A look at India’s biometrics identification system: digital APIs for a connected world

India’s digital infrastructure transformation is enabling new services and APIs. Explore how India’s digital citizen app works and how India is securing data and authenticating users.

The consistency of government-issued identification ensures connection to government benefits and financial services, limiting fraud, waste, and abuse. The lack of a national identification system has restricted access to public sector goods and services. Indians had struggled when, obtaining a driver’s license or even enabling a mobile phone, when they didn’t have identification. That has changed. India has emerged as a global leader in digital identification and has established the largest database of biometrics in the world.

Aadhaar: a national identification system

The classic debate between open free markets and imposed central control by government is the foundational quandary of democracy. A question that comes to the forefront is: have open markets offered a road to growth and prosperity or is a central operator a crucial element of the economic balance?

Regardless of the side of this debate upon which you fall, in both scenarios, a method for building an implied architecture for trust is required. A key tenet of setting the foundation for economic growth in a free and open market is to ensure citizen identity. Social healthcare, government entitlement programs, immigration, school admissions, and electoral voting all demand an identification system to minimize abuse of economic systems.

1.1 billion enrolled and growing

The Unique Identification Authority of India (UIDAI) is the world’s largest voluntary national identification number project, intended to cover 1.34 billion residents at project completion. Aadhaar is a voluntary, unique identification document program similar to the smart card program of France. Unlike the United States Social Security Numbers (SSN) program, the Chinese ID Card program, and the Chile ID Card – RUN program, UIDAI is not mandatory, but rather a voluntary government program.

The Aadhaar programs have made impressive strides towards a vision of providing all residents of Indian an identities. As of February 2017, 1.1 billion Aadhaar cards had been issued, an initiative that began in 2009. A struggle that started with the lofty goal of issuing 100 million cards within the 1st year, now has built a national system that can process 1.5 million applications a day.

Aadhaar is industrial grade and handles 15 million transactions daily.

The race to a unified payment system

The Unique Identification Authority of India created with the objective to issue Unique Identification numbers (UID), named as “Aadhaar.” Even before the first Aadhaar card was issued, in 2010, the UIDAI launched the Aadhaar Auth API. By 2011 the National Payments Corporation of India (NPCI) launched the Aadhaar Payments Bridge and the Aadhaar Enabled Payments System which uses the Aadhaar number as a central key for the electronically channelizing of government benefits and subsidies. Through 2012 the UIDAI, eKYC, a paperless Know Your Customer (KYC) process, allowed businesses to perform the “Know Your Customer” verification process digitally using Biometric or Mobile OTP. It is within the eKYC process that the identity and address of the subscriber are verified electronically through Aadhaar Authentication. The Aadhaar eKYC was initiated to reduce the delays with attestations and other verifications resulting in faster processing and issuance of digital certificates.

In 2015 the Government of India, Controlled of Certifying Authorities launched eSign as an open API. This new API enables an Aadhaar card holder to sign a document digitally. The Ministry of Electronics and Information Technology (MeitY) also launched DigiLocker, a platform for issuance and verification of documents and certificates in a digital way, thus eliminating the use of physical documents.

For reference-accessing a document available on DigiLocker has five simple steps:

  1. Sign up: register with mobile
  2. Sync: use your Aadhaar (card) to sync
  3. Request: get documents from issuers
  4. Share: exchange documents with requesters
  5. View: get documents verified by requesters

Today, DigiLocker has 4.5 million registered used with over 6.6 million uploaded documents, and 1.6 billion issued digital documents.

Lastly, in 2016 the National Payments Corporation of India enhanced their Unified Payments Interface, an advanced public payments system. This interface may be the backbone that catapults India into a cashless society by 2020.

India’s emerging API integrations

IndiaStack is a set of APIs that allows governments, businesses, startups and developers to utilize an India’s digital infrastructure. IndiaStack provides four technology layers: (1) presence-less, (2) paperless, (3) cashless, and (4) consent. Already some interesting APIs and products have emerged to leverage the IndiaStack suite of APIs.

There are several sandbox servers available to test functionality across the Aadhaar network. The Aadhaar Bridge Developer Kit enables developers to build apps with Aadhaar integration using one seamless platform. eMudhra eServices comprises of eSign and eAuth services that enable electronic paperless signing and authentication services. This service allows residents to sign any document electronically without going through the hassle of signing a document physically or with a dongle based digital signature – no physical signature required. AuthBridge offers an array of services like employee background check, risk assessment, and talent solutions. OnGrid is a consent-based trust platform that modernizes verification and background checks in India by linking an individual’s data, documents, verification reports, testimonials and references to the residents’ 12-digit Aadhaar number, for a faster and cleaner access to true identity and background. Aadhaar API provides authentication (anyone using their Aadhaar number and biometric, OTP, and demographic data instantly), eKYC (onboard anyone quickly by retrieving their proof of address and proof of identity using Aadhaar based KYC), and eSIGN (get contracts and documents digitally signed by your customers or business associates using Aadhaar). Digio is another document signing app that allows residents to sign agreements, IDs (driving licenses, PAN card, passports), approvals and letters, and self-attestations.

The Aadhaar API

How does India manage the vast amounts of data flowing through the identification system supporting 1.1 billion residents? There are a lot of articles addressing in general terms how the Indian identification systems work. However, I was hard pressed to find out any specifics about how authentication or data security and privacy was managed.

That was until I stumbled upon the Aahdaar Authentication API Specification. This specification outlines the how software professionals and vendors can in incorporate Aadhaar authentication into their applications. This is an API that will soon reach across India.In the following two sections I’ll explain how authentication and data security is managed by the Indian identification system.

Authentication and data security

Data blocks are encrypted with using an AES-256symmetric algorithm (AES/ECB/PKCS7Padding), and the session key is encrypted with 2048-bit UIDAI public key using an asymmetric algorithm (RSA/ECB/PKCS1Padding). Sessions keys are one-time only use and are not used across transactions. (There is a rather technical exception, but for the article’s continuity, we’ll omit that description. But you’re welcome to explore it if interested.)

The encryption flow also accepts multi-factor authentication using one-time pin (OTP) and could also be used in conjunction with biometrics.

Access to the Aadhaar Authentication API is categorized into two broad workflows:(1) registered devices and (2) non-registered devices. For this discussion, we’ll focus on the most likely access point, a registered device.

The encryption flow has nine steps which I’ll summary for brevity.

  1. Request key: Aadhaar Number, demographic, and biometric details enter into the application, and the Aadhaar authentication server sends out a one-time-pin to the resident’s registered phone
  2. Generate key: the application generates a one-time session key
  3. Encode data: an authentication “data” XML block is encrypted using the one-time session key and then encoded (base 64)
  4. Encrypt data:  the session key is encrypted with the UIDAI public key
  5. Send data: the encrypted block is sent along with a keyed-hash message authentication code (HMAC) to the authentication server
  6. Construct XML: the authentication server composes the XML for API input, including a license key
  7. Decrypt data: the authentication server decrypts the data with the UIDAI private key, then the data block is decrypted using the session key.
  8. Biometric factored: the residents biometric, demographics information and optional one-time-pin is factored in during the match
  9. Response confirmation: Aadhaar authentication server responds with a yes or no as part of the digitally signed response XML.

The introduction India’s national identity system establishes the foundation for an integrated government. In my most recent paper titled An e-Government Interoperability Framework to Reduce Waste, Fraud, and Abuse, I present a practical approach for how separate entities can share information within the United States through an e-Government Interoperability Framework. Interoperability is the key to a connected government, and India has taken the first step. A connected government is a government where the residents can access services such as healthcare, school admission, voting, and government programs seamlessly across government entities. With 1.1 billion issued Aadhaar cards racing towards 1.3 billion, it’s evident that the Aadhaar program is a vision that be realized. A program for the betterment of all Indian residents. Other countries, including the United States, would be well advised to pick up a lesson or two from the largest identification and biometrics program in the world.

Microservice ecosystems for healthcare

Peter Nichol
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Microservice ecosystems for healthcare

Patients will experience profound positive impacts as the advantages of microservices become better understood by healthcare technology leaders.

Google, Amazon and Soundcloud all have successfully deployed microservices. Let’s modernize healthcare applications with microservices.

Microservice architecture transfers healthcare providers and payers from one large application into smaller applications. These little applications or “micro” applications provide specialization using service-oriented architectures (SOA) by building dependent and flexible components. These micro pieces are not simple CRUD (create, read, update, delete) services — they have responsibilities.

Microservices combine lightweight mechanisms that offer scalability (Netflix supporting 800 different devices and 1 billion calls a day) and can support a range of platforms and interactions (the web, mobile, IoT, wearables).

The world of microservices

There are many reasons why microservices are valuable for healthcare. Before we jump into those reasons, let’s define the ecosystem that makes up the world of micro services.

A dynamic response to changing business conditions

Microservices provide agility and align well with changing business needs that require automation and the ability for functionality to be recomposed. The benefit of intrinsic interoperability with industrywide standards (HTTP and JSON) ensures that your technology is enabling your business to solidify your competitive advantage.

Microservices work off a three-layer system: system APIs (core business capabilities), process APIs (orchestration and choreography of components) and experience APIs (adaptable processes and configurable options). As patient engagement, sustainability, and outcomes prove ever more critical, the ability to micronize your healthcare environment will become a best practice in healthcare. The speed of delivery, accelerating innovation capabilities and new models of care today are prerequisites for a functional and efficient business operation.

Microservices for healthcare enable this vision.

Avoiding the snowball

Monolithic applications like the large electronic health record systems we know and love, eventually snowball into unreasonably large systems. The effect is that problems quickly snowball, out of control. Simple changes need to be made in multiple locations. Various systems across a healthcare ecosystem are running on different versions or service patients using entirely different and unconnected systems. Value is siloed.

What’s our solution to this problem? Our solution is that we build the functionality over and over again. We try to “reuse” components, but for the most part, they are constructed initially by vendors and then modernized — and that means another bill for similar work. Moving away from limited-reuse applications enables organizations to slide move toward the edge of innovation — where the most value occurs.

Microservice providers acknowledge there are tradeoffs when leading initiatives that require scale (multiple location installations), including the following:

  • Service discovery and documentation
  • Fault tolerance
  • Quality of service
  • Security
  • Request traceability
  • Failure triage

Start exploring the value of microservices

It’s always difficult when exploring new areas you’re unfamiliar with. Here are a few steps to help jump-start the journey of incorporating microservices into your healthcare environment.

  1. Identify potential microservices categories where you may find value.
  2. Define the scope of responsibility for the identified microservices.
  3. Consider the type of information that will be transmitted.
  4. Associate business processes with the technical functionality defined.
  5. Link technical processes to the business processes.
  6. Research capabilities that are ahead on the business road map — capabilities  that are not offered today but are desired. The following steps are typically done with tools, not manually.
  7. Design the micro service starting with the API definition and elaborate how the service will be consumed (REST or event-driven)
  8. Develop a service mocking or simulation. This step is also known by isolation, simulation or virtualization. In essence, you’re building something that works as something else.
  9. Deploy the microservice. This is where we transition from deploying to multi-tenant environments like JBoss AS or Tomcat and leverage IaaS automation frameworks such as HashiCorp or Chef and virtualization technology such as Xen or VMware.
  10. Manage container systems. Conflicts and container integration must be proactively managed. Pivotal Cloud Foundry and Mesosphere DCOS have recognized this gap and are evolving to address the need.

Best-of-breed healthcare solutions

This year, your team will identify new technical capabilities. They will assess how these skills will align to the predefined needs of the business. As a healthcare leader, what do you expect out of this analysis? What have we always expected? We expect a recommendation — a single recommendation.

When was the last time your team identified, assessed and presented options and the result was a set of five to eight products that worked together and provided a unified best-of-breed solution? I’d say it probably hasn’t happened in the past 30 days and likely not even within the past year. Whether you’re assessing a healthcare medical record solution or a pure desktop product used by clinicians, everyone wants simplicity.

Unfortunately, in today’s knowledge-rich world, one solution rarely provides all the answers. As a result, we “fit.” We fit our solution into whatever problem hole we find. The solution rarely fits the need perfectly, yet we just cram the solution into the problem space. The action correspondingly has a ton of white space where the solution didn’t solve the intended problem (business or technical).

Delivering the value of simple

The most logical microservices uses are attributable to business processes or transactions. Microservice responsibility goes beyond pushing data. Each service is discrete and encapsulates a set of responsibilities. These responsibilities may relate to a business domain such as claims or billing. However, they also could relate to technical domains such as operating systems or network performance.

The benefit of deploying microservices is the micro scale of functionality that is agonistic to a particular domain or subdomain such as claims reconciliation. The patient name, account number and balance may also be applicable across other business areas such as patient entry, patient discharge or utilization. Microservices begin with business-oriented designs commonly in the form of APIs (business interactions to access information).

Adaptability, loose coupling, autonomy, fault tolerance, composability and discoverability each offer the advantage of reuse — a core principle supporting the value of microservices design. Define the problem first. Savvy healthcare pioneers have already discovered that microservices help solve problems by getting back to simple.

Darwinian insights on innovation and competition

Peter Nichol
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Darwinian insights on innovation and competition

Variation, selection and competition are the challenges of navigating today’s digital ecosystem of value. Identify the struggle between individuals and competitors to discover tomorrow’s game-changers. Innovation is the modern struggle for existence. Will your organization survive?

Consumers buy your products, services and interactions for reliability. Partners sought your alignment for greater stability. Employees joined your company for predictable results. Disruptive innovation can be identified when best practices no longer produce predictable results. Our modern knowledge-intensive economy depends on organizational capabilities. Is your organization having trouble identifying why the margin is eroding? Disruption in disguise may be the answer.

Struggle for existence

Charles Darwin’s The Origin of Species is unquestionably one of the greatest works in human intellectual history. In this seminal book, Darwin develops the argument of why the theory of special selection is incorrect and why the theory of natural selection is more favorable. Eventually, reputable scientists arrived to acknowledge that evolution, the transformation of species over time, had in fact occurred. Darwin elaborated that variance is not an anomaly but rather an inevitable result of orchestrated processes. Causes of variability and the difficulty in distinguishing between varieties and species were not only challenges for Darwin. Today, a complex ecosystem of offerings makes the identification of value-based innovations difficult to delineate in markets with multiple offerings.

Buried under the struggle for existence, many innovators incorrectly assume that natural selection requires competition among individuals. Darwin defines this struggle not between individuals as competitors but in a metaphorical sense where predation, parasitism or environmental conditions dictate a new struggle. Natural selection eliminates competition. All modern innovation organizations should pay attention to lessons of selection in the struggle for existence — a modern struggle for variability through innovation and predictable results. Industries are looking less to their neighbors and more toward unrelated industries for innovation insights. You’re not competing with your business neighbor.

Natural selection redefining the rules

Industry leaders are searching to discover tomorrow’s game-changers. Will a new technology improve efficiency? Is the current business model changing? How do we compete tomorrow in this explosive sharing economy? There are multiple methods to ensure corporate survival. The accepted method favors players that evolve and adapt. The winners define new rules and establish new games.

This year will unlock opportunities — ones that were not afforded last year. The dawning of the new year also will bring challenges previously unseen. Start with these questions before you set your organizational agenda.

  1. Is your organization creating and capturing value?
  2. Does your organization not only find the right strategies but make good decisions when selecting future strategies?
  3. Is your organization in competition or cooperation? For example, is your organization building walls for the competition or establishing relationships with unlikely allies?
  4. Are you playing an old game, or are you redefining a new game?
  5. Has your organization clearly identified complimentors (the situation in which customers and suppliers play symmetric roles)?

Natural selection may preserve favorable variations and reject injurious variations. Like the natural selection of animals, all inferior businesses are not immediately destroyed; they evolve out of existence. Darwin suggested that natural selection is “the daily and hourly scrutinizing, throughout the world, [of] every variation, even the slightest, rejecting that which is bad, preserving and adding up all that is good.” Isn’t this happening in business — every hour of every day? The change we experience in business is natural selection. Consider the value your organization adds, as environmental conditions change. Is your organization evolving out of existence?

The evolution of disruption

Several mistletoe plants growing on the same branch of a host tree may struggle for existence. It might be truer that the struggle for existence is not against the thousands of seeds of the same kind, or against other fruit-bearing plants, but against any attempt to devour the seeds and thus prevent dissemination. Disruption is not an event; it’s evolution, a transformation of convenience. Aspects of your business are transforming as did cloud computing, consumerism and mobile — focus beyond the seeds of your company and observe the broader struggle for existence.

The innovation duel: game theory and product launch timing

Peter Nichol
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The innovation duel: game theory and product launch timing

Game theory has consumed innovation. The duel of innovation depends on timing, and timing depends on how you play the game. Take control of your game and play with new strategy in the new year.

Old west cowboys personalized the concept of honor. In a refined society, the art of politeness demanded the withdrawal from overt acts of violence. The solution? The western duel. Where do you stand? When do you start? What are the rules? The code of honor of a duel and the code of innovation have remarkable similarities. Let’s explore a few.

Game theory

Game theory is the study of strategy decisions. Understanding the other player’s perspective is central to game theory. Dueling swordsmen, dueling gunfighters and dueling innovators all have common ground. A strategy decision faces them all: to lead or not to lead?

Bonanza (1959-1973), The Rifleman (1958-1963) and Gunsmoke (1955-1975) rank right up there with the best TV westerns of all time.  We followed the adventures of Ben Cartwright on the ranch in Bonanza. We felt the struggle of Lucas McCain as he raised a son while battling the wild west of New Mexico in The Rifleman. And of course, Marshal Matt Dillon kept his eye on lawlessness in Dodge City with the help of saloon proprietor Miss Kitty Russell and Doc Adams in Gunsmoke.

Each of those great westerns had one element in common with modern innovation — the duel.

The swords of the 17th and 18th centuries were quickly replaced by pistols throughout the late 18th century and into the 19th century. Mainly practiced in early modern Europe, duels did make an appearance in the United States. Duels were not necessarily fought to eliminate the other person, but rather to restore honor by demonstrating a willingness to risk one’s life for something. Between 1798 and the American Civil War, the U.S. Navy lost two-thirds as many officers in duels as it did in combat at sea. Dueling was a big problem.

Fortunately, duels are no longer required to become a leader of the country, as was the case for Andrew Jackson in his bid to become the seventh president of the United States. Surprisingly, the game theory of a duel and a product launch are similar in many ways. When Andrew Jackson had to duel against Charles Dickinson, what was his strategy? How did Jackson determine when to fire? Dickinson was a famous duelist and a known marksman. Jackson determined to let Dickinson fire first, hoping that his aim might be spoiled by his quickness. Dickinson did fire first, hitting Jackson. Jackson carefully took aim and hit Dickinson in the chest, inflicting wounds that later caused Dickinson’s death. In 1832, the distance of a duel was 35 to 45 feet and both contenders were stationary. However, stagnation is not a characteristic of innovation — innovation is in motion. Playing the game of innovation revolves around timing.

The duel of innovation

In the Art of Strategy, Avinash Dixit and Barry Nalebuff explain the game theory of “The Safer Duel.” They explore how to plan a strategy for a duel.

Let’s reframe the topic of this discussion from dueling pistols to dueling innovators, because you probably don’t need a strategy for planning a duel but it would be useful to understand when to launch a product, a service or an interaction. If you launch too early, you’ll miss the market. Launch too late and the competition will eat you. When do you pull the trigger on your launch?

Think of two innovative companies that are miles apart but slowing walking toward each other — the duel of innovation. In this example, each company is launching a product (though it could be a service or an interaction) and both companies are capable of launching the product. In this example, each company would wait to launch until its probability of launching effectively is equal to the other company’s chance of a failed launch. The only factor that matters in determining a strategy is the ultimate chance of success. At the ideal inflection point, the probability of a successful launch is a half for the company launching and a half for the company not launching. Logical deduction tells us that survival is best achieved when at a distance (or timing) where the launching company has a half chance of success. Interesting isn’t it? As your new year begins, the survival of your company’s new product offering hangs on the timing of the launch. Too early you’ll miss the market. Too late you’ll be beaten.

Playing to win

While you may not subscribe to the theory of “playing to win,” it’s doubtful you support the theory of “playing to lose.” Game theory can help balance the risks of launching a new product after your competition. Whether you’re playing tic tac toe or planning the next launch of your company’s flagship product — deep down everyone wants to win. Acknowledge that you’re in a duel of innovation.

 

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