A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

As I mentioned in the previous part of the analysis, cyberattacks represent a new form of attack, and attempting to frame cyberattacks in terms analogous to those of traditional, real-world attacks has proven to be problematic. Part of the reason for the difficulties stems from the unique properties of cyberwar as compared with those encountered in cases of real-world war. This section discusses some of the more significant unique properties of cyberwar that distinguish it from traditional war.

Cyberwar is Doubly Dangerous

In cyberwar, offensive weapons cannot be distinguished from defensive weapons, and cyberwar tends to favor offense. Together, these two properties make cyberwar “doubly dangerous” as per Jervis’s characterization depicted in Figure 2.

In cyberwar, offensive weapons cannot be distinguished from defensive weapons, because the same technology that is used for offense is also used for defense. As Randall R. Dipert describes it,

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In Part 1: Game Theory Basics of War, I described the three potential options players may take in traditional war: don't arm, arm, or attack. I described the benefits and costs associated with arming for defense, as well as the benefits and costs associated with arming for offense or attacking. I indicated that there are ambiguities in interpretaions by players associated with the actions  other players are taking: sometimes arming can increase a player’s security, while other times, arming can decrease a player’s security. I explained that there are two crucial variables that contribute to the ambiguity: (i) whether defensive weapons can be distinguished from offensive weapons, and (ii) whether defense or offense has the advantage.

In Part 2: Defining Cyberwar, I indicated that cyberattacks represent a new form of attack, and attempting to frame cyberattacks in terms analogous to those of traditional, real-world attacks has proven to be problematic, in part because cyberwar represents a new kind of war attempts to provide parallels between traditional war and cyberwar has proven to be problematic. I distinguished cyberattacks, cyberwar, and cyberterrorism from one another based on (i) whether the actor was government or civilian and (ii) whether the motivation was personal/commercial or political in nature.

In Part 3: Unique Properties of Cyberwar, I discussed some of the more significant unique properties of cyberwar that distinguish it from traditional war: (i) Cyberwar creates a security dilemma since (a) it's difficult to distinguish offensive from defensive actions in cyberspace, and (b) offense has the advantage over defense; (ii) in cyberspace it's difficult to know who the perpetrator of a cyberattack is; (iii) in cyberwar, there are generally no human injuries or death; and (iv) many cyberweapons are one-time-use in nature.

In this section, the last section of the analysis, I explore several defensive and offensive strategies for players engaged in cyberwar.

Defensive Strategies

Decrease Incentives to Attack

As I mentioned in the previous section, cyberwar has the potential to create a security dilemma, since (i) offensive weapons cannot be distinguished from defensive weapons and (ii) cyberwar tends to favor offense. One player might simply be trying to defensively arm itself against cyberattacks from other players. However, by doing so he may induce insecurity in other players that might lead them to arm themselves for cyberwar and possibly even attack the player preemptively.

To mitigate against the potential for a security dilemma – and particularly against the possibility of preemptive attacks by others – a player who wishes to arm himself to defend against cyberattacks can increase the transparency of his actions to better clarify (signal) his intentions to other players.

Price discrimination may be defined as selling the same thing to different people for different prices. Price discrimination can take many forms, such as volume discounts, price premiums, or market segmentation. Suppliers regularly use many different forms of price discrimination, which people generally don’t object to.

Some suppliers use dynamic pricing, a sub-category of price discrimination, in which prices change over time with market conditions. Consumers have been used to the fact that prices for airline tickets and hotel rooms change constantly, and that different people end up paying different prices for a seat on the plane. While uncomfortable with the practice, consumers have generally come to accept this type of dynamic pricing (what choice do they have?).

Over the past few years, dynamic pricing has become more widely used by sellers as a means of supplementing shrinking margins in an increasingly competitive world. As more information becomes easily available in digital form, pricing algorithms used to support dynamic pricing systems have been able to draw upon more and more information to hone prices and increase profits.

A more controversial sub-category of dynamic pricing is personalized pricing, which uses personal information on each customer to tailor prices specifically to that customer.

This analysis examines the different types of price discrimination, how they increase profits, why they are becoming increasingly prevalent, and some emergent issues surrounding their use.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

Infectious diseases are the second leading cause of death worldwide. Since their discovery in the 1940s, antibiotics have been the primary treatment for infectious diseases. However, over time, many diseases have become resistant to the antibiotics that have been used to treat them, causing tens of billions of dollars in added treatment costs and millions of deaths globally.

This analysis analyzes the factors (game) involved in (i) the supply and use of antibiotics to treat disease, and (ii) the eventual resistance of many of these diseases to the use of antibiotics.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In Part 1 of this analysis, I provided a brief description of the Microbial Resistance Game, and I described the various pathways of microbial resistance to antibiotics.

In this section I describe the players involved in the Antimicrobial Resistance Game (as illustrated in Figure 1), together with their incentives.

Definition of Net Neutrality

The Heart of the Matter

Overview of the Net Neutrality Game

Outcome of the Game: Per-User vs. Per-Usage Internet Fees

A recent court decision struck a blow against net neutrality.  One account of the decision appeared in the WSJ, “Court Backs Comcast Over FCC on 'Net Neutrality’” by Amy Schatz:

Brief Recap of Part 1

Understanding Deep Packet Inspection

Understanding Broadband Services

Using DPI to Manage Internet Traffic

Outcome of the Net Neutrality Game — Take 2

Brief Recap of Part 1

In Playing the Net Neutrality Game, Part 1, I presented a discussion of net neutrality that focused on the common carrier aspect of the issue.  That is, proponents of net neutrality argue that the Internet, is an essential component of the nation’s communication system, and as such

Internet access providers should not discriminate with regard to what applications an individual can use, or the content an individual can upload, download, or interacted with over the network. Individuals acquiring services from Internet access providers should be able to use the applications and devices of their choice, and interact with the content of their choice anywhere on the Internet.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

One of the biggest issues currently in the media is the subject of online adblocking. Adblocking involves the installation and use by Internet Users of adblocking software on their web browsers, so as to prevent advertisements from appearing on websites. Many are predicting that adblocking software will seriously hamper the provision of content on the Internet by preventing the use of the highly popular, ad-based, Internet business model. For example, recent headlines include:

“AdBlock Plus won’t bring down the web, but the bell is tolling for current business models”

“Blocking ads will force online publishing to change”

“Should ad blockers be legal?”

“Google lost billions of dollars last year thanks to Adblock extensions”

A recent announcement by Apple further heightened the controversy over the use of adblocking software. In early June the Company announced that it would provide adblocking software for the next version of its Safari browser for use on mobile devices (see for example Joshua Benton, “A blow for mobile advertising: The next version of Safari will let users block ads on iPhones and iPads”).

What’s even more interesting is that an entire ecosystem is popping up around the use of adblock software, for example as different players introduce new adblocking software for Users and new Service Providers introduce analytics to help Content Providers understand the extent of adblocking use by Users.

This analysis examines the dynamics involved in the Online Adblocking Game, which includes such players as Online Users, Content Providers, Advertisers, and Adblock Software and Services Providers. The first part of the analysis will examine trends in online ad revenues and ad pricing models, as a backdrop for analysis of the Adblocking Game. The second part of the analysis will introduce adblocking and describe its use. And the third part of the analysis (or perhaps third and fourth) will discuss the adblocking game.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In Part 1 of this analysis, I described recent trends in Internet advertising and Internet advertising pricing models. In this part, Part 2, I introduce adblocking and describe its use, together with the two major controversies surrounding the use of adblocking software.

About Internet Adblocking

According to Wikipedia, “Ad filtering or ad blocking is removing or altering advertising content in a webpage.” Most browsers allow basic forms of adblocking through the Options or Preferences settings. And users have been using browser settings to block pop-up ads in particular for quite some time. However, the adblocking that has been discussed in the media lately involves using browser add-ons, which enable users to completely block out all ads, not just pop-ups.

This section starts with a brief description of how adlocking works, goes on to describe the benefits of using adblocking, trends in adoption of adblocking, and then ends with a discussion of the controversies surrounding the use of adblocking.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In Part 1 of this analysis, I described recent trends in Internet advertising and Internet advertising pricing models. In Part 2, I introduced adblocking and described its use, together with the two major controversies surrounding the use of adblocking software. In this part of the analysis, Part 3, I describe the players and their motivations in the Adblocking Game. I also present a model of the Adblocking Game. In the last part of the analysis, Part 4, I will provide a discussion of the interesting aspects of the Game.

The Online Adblocking Game

Figure 15 presents the various players in the Online Adblocking Game. I describe below each of the (groups of) players, together with his motivations in the Game.

Figure 15

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In Part 1 of this analysis, I described recent trends in Internet advertising and Internet advertising pricing models. In Part 2, I introduced adblocking and described its use, together with the two major controversies surrounding the use of adblocking software. In Part 3 of the analysis, I described the players and their motivations in the Adblocking Game, and I also presented a model of the Adblocking Game. In this last part of the analysis, Part 4, I discuss some interesting aspects of the Game.

Discussion of the Adblocking Game

Ad-Based Models Create Misaligned Incentives

A big problem with ad-based business models is that they create incentive misalignments in the business process. More specifically, when a business relies on advertising revenues, it ends up putting the needs and/or interests of Advertisers and/or Users in front of the needs of the business. Ian McAllister explains this in “What's wrong with an ad-supported business model?”

Incidents of hackers ("Black Hats", that is, those with nefarious intentions) breaking into technology systems are certainly nothing new. But each new report reminds us that we’re all vulnerable to falling victim to some hacker’s attack on some system that would cause us harm.

Hackers hacking into systems that contain our financial information can cause us financial harm. Examples include incidents last year in which hackers stole customer credit card information from Target and Home Depot.

Hackers hacking into systems that contain our personal information can cause us reputational harm. The recent hack of the Ashley Madison website, together with the release of participants’ identification provides a good example of this.

Hackers will also increasingly be able to cause physical harm, for example, by hacking into automobile systems.

While we cannot expect systems developers to make systems completely impenetrable, we can surely expect them to take sufficient efforts to make their systems reasonably robust to hacker attacks. Unfortunately, in many environments, systems developers simply do not have the incentives needed that would encourage them to make their systems nearly as robust as we – the potential users and victims of attacks – would like them to.

This analysis examines the game between Systems Developers, Hackers, and Users (Victims) to determine when developers have too few incentives to make their systems robust and what might be changed to incentivize them to take more care.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In order to encourage big bets, Jeopardy is winner-take-all … Only the person in first place keeps their total at the end of the game. There’s a very powerful incentive to be aggressive... The most important thing isn’t the absolute number of dollars you have on the board. It’s how strongly you’re beating the other players.

– Julio Rodriguez, “Poker Player Crushing Jeopardy With Unorthodox Strategy”

Winner-Take-All markets – where small groups of companies or individuals receive an over-sized share of earnings – seem to characterize an increasing portion of markets in today’s economy.

There are actually two, distinct, simultaneously-occurring phenomena that have been enabling fewer individuals to extract larger portions of wealth from global markets. The first phenomenon is a shift in leverage that is enabling fewer individuals to extract more wealth from certain types of markets; that is, it’s an intra-market shift. The second phenomenon is an increase in the size of markets due to the erosion of barriers that had previously prevented regional markets from consolidating globally; that is, the second shift is an inter-market shift.

What are the factors driving these two sets of phenomena? Are the resulting WTA markets good or bad for society? If/when they’re bad, how can we mitigate against the effects? These are the issues that this analysis addresses. In Part 1 of the analysis, I describe the forces contributing to the creation of Winner-Take-All markets. In Part 2 of the analysis, I provide some examples of WTA markets, together with ways of mitigating negative effects in WTA markets. In Part 3 of the analysis, I discuss some strategic issues associated with WTA markets.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In Part 1 of this analysis on Winner-Take-All (WTA) markets, I described the forces contributing to the increasing incidence of WTA markets that we've been seeing over the past few decades in global economies. More specifically, I described two, distinct phenomena that have been enabling fewer individuals to extract larger portions of wealth from global markets: (i) an intra-market shift in leverage that has been enabling fewer individuals to extract more wealth from certain types of markets; and (ii)  an an inter-market shiftcausing increases in the size of markets, due to the erosion of barriers that had previously prevented regional markets from consolidating globally.

In this part of the analysis, Part 2, I provide some examples of WTA markets, together with ways of mitigating negative effects in these markets.

In the last part of the analysis, Part 3, I discuss some strategic issues associated with WTA markets.

Examples of WTA Markets

This section provides some examples of Winner-Take-All markets, together with indications of the specific forces (described in the previous two sections) that have contributed to the formation of WTA phenomena in these markets.

A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.

In Part 1 of this analysis on Winner-Take-All (WTA) markets, I described the forces contributing to the increasing incidence of WTA markets that we've been seeing over the past few decades in global economies. More specifically, I described two, distinct phenomena that have been enabling fewer individuals to extract larger portions of wealth from global markets: (i) an intra-market shift in leverage that has been enabling fewer individuals to extract more wealth from certain types of markets; and (ii)  an an inter-market shiftcausing increases in the size of markets, due to the erosion of barriers that had previously prevented regional markets from consolidating globally.

In Part 2 of the analysis, I provided some examples of WTA markets, together with ways of mitigating negative effects in these markets.

In this last part of the analysis, Part 3, I discuss some strategic issues associated with WTA markets.

With the globalization of markets, the value potential for any product or service is much larger than it used to be, when markets were local, regional, or even national. Let’s consider some of the different factors that contribute to a product’s potential value, together with strategic implications for WTA markets: (i) market size, (ii) predominance of social effects, (iii) availability of complementary assets, (iv) power of path dependency, and (v) use of information filtering.

The Yelp Controversy

Yelp is a social media site that hosts information about local businesses and lets customers post ratings and reviews about their experiences with those businesses. Yelp uses a proprietary algorithm to sort through posted reviews and filter out those that might be fake or inauthentic. Reviews that do not pass muster are relegated to a separate folder and not used to calculate businesses’ average ratings scores, which are prominently displayed to users.

Yelp generates revenues to support its business primarily through sales of ads to local businesses that appear on Yelp’s website.

Business owners have alleged that in an attempt to coerce (extort) businesses into advertising on Yelp’s site, or in retaliation for ceasing to advertise on its site, Yelp manipulates those businesses’ reviews by either

  i.  Filtering out good reviews and relegating them to the “reviews not recommended” folder, thus hiding them and lowering businesses’ average star ratings,

 ii.  Highlighting bad reviews of the businesses, and/or

iii.  Creating fake bad reviews.

Business owners allege that Yelp’s manipulations of the businesses’ reviews have resulted in lost sales for their businesses.

Yelp vehemently denies accusations that it extorts business owners or manipulates the filtering of reviews.

This analysis examines the Yelp Ratings Game. First, I describe the shift in customer relations that the advent of social media has engendered. Next, I describe how the Yelp site works. Then I discuss possible reactions by business owners to having ratings and reviews by users about their businesses posted online and perhaps manipulated to the business owners’ detriment. Finally, I discuss some other interesting issues related to the Game.

A Very Brief History of the Evolution of Choice

The recently awarded prize by Netflix for coming up with a more accurate prediction algorithm has been on my mind lately.To be more precise, what I’ve been thinking about is what the winners have achieved with their algorithm.That is, why is being able to predict what people will like so important, or in more “useful” terms, what is the value of being able to predict what people will like?

Along those lines, I’ve been thinking about how market offerings have evolved to provide people with choices, and then later to help them select among their choices.

Information Sets

Faulty Sampling

A recent article in the NYT, “Weighing Medical Costs of End-of-Life Care” by Reed Abelson, uses the cases of two hospitals, UCLA and the May Clinic, to discuss the issue of how to provide cost effective medical care:

[C]ritics in the Obama administration and elsewhere who talk about how much money the nation wastes on needless tests and futile procedures. They like to note that U.C.L.A. is perennially near the top of widely cited data, compiled by researchers at Dartmouth, ranking medical centers that spend the most on end-of-life care but seem to have no better results than hospitals spending much less…

According to Dartmouth, Medicare pays about $50,000 during a patient’s last six months of care by U.C.L.A., where patients may be seen by dozens of different specialists and spend weeks in the hospital before they die. By contrast, the figure is about $25,000 at the Mayo Clinic in Rochester, Minn., where doctors closely coordinate care, are slow to bring in specialists and aim to avoid expensive treatments that offer little or no benefit to a patient…

Suppose a friend told you that he was planning on doing a TED Talk, and he asked your advice on how to make his talk one of the most popular TED Talks out there. What would you tell him?

This is exactly the type of question Data Scientists seek to answer. The way Data Scientists approach such a problem is to gather information on past TED Talks and analyze that information to see which factors describe only the most popular TED Talks, and not also the less popular Talks. For our purposes, we’ll define “popular” TED Talks as Talks that generate a lot of views.

So then following the Data Scientists’ route, we obtain a database that contains all TED Talks posted on the TED website from its inception in June 2006 through September 2017. There are 2,550 talks. The distribution of views per talk across all the different talks is presented in Figure 1.

Figure 1

Underlying Issue

Trends in Total US Healthcare Expenditures

Trends in Personal Healthcare Expenditures

Trends in Healthcare Expenditures by Condition

In Sum

Underlying Issue

The total annual costs of healthcare paid by each individual is the sum of the healthcare premiums he pays and the out-of-pocket costs he incurs:

Total Cost of Healthcare = Insurance Premiums + Out-of-pocket Costs

Roughly speaking, the annual insurance premium an individual pays is the average of the total annual costs paid by his insurance company for the healthcare costs incurred by all individuals in his (age) group. What this means is that if the healthcare costs of one individual rise, then that individual does not bear the full burden of the costs increase, but rather, the burden is shared by all members of the group. This is the very nature of risk-pooling, and it works fine when all the members in the group face the same risks.

Out-of-pocket costs for healthcare depend on the type of coverage an individual has, plus the amount of healthcare individuals use.

Moving on, the amount of healthcare an individual will use/need during the year depends on several factors:

• Genes: People will end up using more healthcare services to the extent that they have “bad” genes.
• Luck: People will end up using more healthcare services to the extent that they have bad luck or are otherwise accident prone.
• Lifestyle: People will end up using more healthcare services to the extent that they have an unhealthy diet, don’t exercise, smoke, don’t take safety precautions (e.g., wear seatbelts), or otherwise lead more risky lifestyles.
• Compliance: People will end up using more healthcare services to the extent that they don’t comply with their doctors’ recommendations (e.g., take medication, lose weight, stop smoking, etc.)

Obviously, people can’t control whether they have bad genes or bad luck. However, they can control the type of lifestyle they live and whether they comply with their doctors’ recommendations.

This begs the following question: To what extent are healthcare costs attributable to factors that people cannot control (bad genes and bad luck), as opposed to factors that they can control (lifestyle and compliance)?

Most people would probably agree to have society (government) subsidize healthcare costs associated with factors people cannot control. However, to the extent that people choose to not control those factors over which they do have power, then to what extent should society be responsible for subsidizing those people’s higher healthcare costs?

Clearly, the issue becomes more important as the costs of healthcare have increased so dramatically over the years.