Facebook and Google have recently being getting flack from users for hosting fake news stories. As Jack Nicas and Deepa Seethataman report, “Google and Facebook Take Aim at Fake-News Sites”

Facebook Inc. and Alphabet Inc.’s Google announced steps to prevent fake-news websites from generating revenue through their ad-selling services, signs that technology companies are moving to address a growing controversy about misinformation on the internet.

The Fake News Game is illustrated in Figure 1, and players’ objectives, actions, and tensions are described briefly below.

I recently published a blog entry on the Net Neutrality Game.  However, I just found out that there is a critical aspect of the net neutrality issue that I failed to understand, namely that involving deep packet inspection (DPI).  Using DPI methods, Internet providers have the capabilities of detecting not only the size of files passing through their lines, but also the content as well.  Clearly, there is a world of difference between managing flows of traffic based solely on file size, and managing flows of files based on the type and content of the files. 

Public vs. Private Information on the Internet

Does Hardware Drive Software, or Vice Versa?

Why Have Past Consortia for Compatibility Failed, and Why Would DECE Now Succeed?

So Are Device-Content Systems Moving Toward Compatibility or Incompatibility?

There are two trends occurring in the digital world that seem to be at odds with one another.  The first is that towards incompatible hardware/device – software/content ecosystems and the other is towards compatible ecosystems.

The Buy vs. Rent Decision

Actual vs. Potential Value of Durable Products

Characterization of Potentially Collaborative Products

There has been an increasing trend toward collaborative consumption, which, which Wikipedia describes as follows:

The term collaborative consumption is used to describe the cultural and economic force away from 'hyper-consumption' to re-invented economic models of sharing, swapping, bartering, trading or renting that have been enabled by advances in social media and peer-to-peer online platforms.

Mapping apps, such as Waze and Google Maps, have created enormous value for users by helping them get to where they’re going faster. As least initially, when few people were using mapping apps, the apps were particularly helpful for individual users in rerouting them around traffic problems. However, now that a large portion of drivers has adopted mapping apps, we’re seeing problems with side routes becoming congested, as everyone is being rerouted through the same detours. So not only is there congestion on the original route -- from where drivers have been re-routed – but there is now congestion in many more additional locations in society – to where drivers have been re-routed.

It turns out that mapping apps are most beneficial to users for dealing with congestion problems when only a few users have adopted them. But they become less useful to users as more people adopt them. That is, the mapping apps exhibit negative network externalities for users when it comes to congestion. At the same time, as more people adopt mapping apps, other members of the community – those who live on the routes through which mapping app users are being re-routed – also suffer, yet another negative externality.

What we have is a game:

• Providers of mapping apps want as many Users as possible to adopt

• Users of mapping apps want as few Users as possible to adopt

• Local Drivers want as few Users as possible to adopt

• Freeway Drivers are happy if Users of mapping apps divert to local roads, if it reduces congestion on the freeway.

This analysis examines mapping apps and other types of resource allocation games.

In an earlier analysis, I looked at some of the dynamics involved in Playing the Online Adblocking Game

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.

My previous analysis focused more on the nitty-gritty of trends in web advertising models, how ad blocking works, and the tensions that adblocking has created for Advertisers, Content Providers (Publishers), and Users.

This analysis focuses on wider trends in Internet access by Users that are causing large shifts in the dynamics between Advertisers, Content Providers (Publishers), and Users. In particular, three trends in Users’ Internet access are causing other ecosystem players to change the way they interact with Users: (i) Users are spending more time accessing the Internet from mobile devices than they are from non-mobile devices; (ii) Users are shifting their gateway to access to the Internet from web browsers to apps; and (iii) Users are spending more time on web platforms than they are on other, more fragmented websites.

In the first part of this analysis I describe how advertising dollars have remained relatively constant over time, even as radically new venues for ads have appeared. I then describe how it was the advent of ad networks that enabled Advertisers to cost-effectively advertise on the Internet, but that the ensuing dynamics have led to a grotesque over-proliferation of ads. I end this part with a discussion of how the ad situation for mobile Users is exacerbated by the unnecessarily poor quality of mobile web browsers.

In the second part of the analysis, I describe how trends in the way Users have been accessing the Internet have led to decreases the effectiveness of previous modes of advertising.

In the last part of the analysis I discuss how the ecosystem may continue to evolve from where it is now.

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

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 how advertising dollars have remained relatively constant over time, even as radically new venues for ads have appeared. I then described how it was the advent of ad networks that enabled Advertisers to cost-effectively advertise on the Internet, but that the ensuing dynamics have led to an over-proliferation of ads. I ended Part 1 with a discussion of how the ad situation for mobile Users is exacerbated by the unnecessarily poor quality of mobile web browsers.

In this second part of the analysis, I describe how trends in the way Users have been accessing the Internet have led to decreases the effectiveness of previous modes of advertising. 

Three General Trends in Internet Use

The way Users have been accessing the Internet has been changing over time in three different ways: (i) by source of access, from desktop (PC) to mobile device; (ii) by mode of access, from web browsers to apps; and (iii) by destination of use: from fragmented websites to platforms.

1.  Source of Access: Users Moving away from Desktop toward Mobile

In 2014 the time spent by Users accessing digital media from mobile devices surpassed the time spent by Users accessing digital media from desktop devices (see Figures 4- 5).

In Part 1 of this analysis on adblocking I described how advertising dollars have remained relatively constant over time, even as radically new venues for ads have appeared. I then described how it was the advent of ad networks that enabled Advertisers to cost-effectively advertise on the Internet, but that the ensuing dynamics have led to an over-proliferation of ads. I ended Part 1 with a discussion of how the ad situation for mobile Users is exacerbated by the unnecessarily poor quality of mobile web browsers.

In Part 2 of the analysis, I described how trends in the way Users have been accessing the Internet have led to decreases the effectiveness of previous modes of advertising.

In this last part of the analysis I discuss how the ecosystem may continue to evolve from where it is now.

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

The Heart of the Matter

Figure 11 provides an illustration of the players and relationships in the Adblocking Game.

Figure 11

Follow Apple’s Lead?

Establish an Online Presence?

Product Differentiation

Offerings with Online Advantage

Offerings with neither Online nor Offline Advantage

Offerings with Offline Advantages

Conclusions

I wrote a previous blog entry on retailing competition between offline and online stores, Will Smartphone-Enabled on-the-Spot Price Comparisons “Upend” Stores’ Business Models?This blog entry takes the previous analysis a step further and considers more explicitly how offline stores might be able to compete with Internet providers. In particular, this examination considers the increasing tendency of consumers to use bricks-and-mortar stores to test out new products, but then buy the products at lower prices on the Internet. How can bricks-and-mortar stores prevent such free-riding or otherwise continue to sustain viable businesses despite the existence of lower prices on the Internet?

“Fake News” has become one of the big afflictions of our times. I just Googled the phrase “fake news,” and it generated 174 million hits. No one seems to know anymore whether or not any reported information is true and/or accurate. This has led people to question the truth of everything, particularly if they don’t like what’s been reported.

Trust in mass media as a whole is declining rapidly across the board. In 1976, 72% of the population had either a great del or a fair amount of trust in mass media. By 2016, that figure had declined to 32%. From Art Swift, “Americans' Trust in Mass Media Sinks to New Low” (and see Figure 1)

Americans' trust and confidence in the mass media "to report the news fully, accurately and fairly" has dropped to its lowest level in Gallup polling history, with 32 saying they have a great deal or fair amount of trust in the media. This is down eight percentage points from last year.

Gallup began asking this question in 1972, and on a yearly basis since 1997. Over the history of the entire trend, Americans' trust and confidence hit its highest point in 1976, at 72...

Figure 1

Access to true and accurate reporting of news and information is pivotal for justice and democracy to prevail. Yet, it’s become extremely difficult to ferret out the truth from news and information reports. How can we address this problem? That is, how do we encourage people to report complete and accurate information?

“Honest” Distortions of Information on the Internet

Not-So-Honest Distortions of Information on the Internet

Propagation on the Internet Promotes Distortion of Information

Governments Use the Internet to Spread Propaganda and Misinformation

Defenses Against Information Distortion

Consequences of Information Distortion

We all know there’s a lot of misinformation on the web. I started reading about this, and I soon discovered that the subject is a lot more complex that I had initially thought. There are two issues that I found particularly interesting:

1. The distinction between “honestly” inaccurate or manipulated information and purposely inaccurate or manipulated information; and

2. The dynamic surrounding how information becomes distorted as it passes from user to user on the Internet.

This analysis discusses (i) each of these two issues, (ii) defenses against being a victim of misinformation, and (iii) consequences of the increasing prevalence of misinformation on the Internet.

Does Apple Dominate the MP3 Player & Smartphone Markets?

Apple iPod & iPhone Sales Timeline

How Did Apple Manage the Growth of Its Ecosystem to Create Value?

Would Even More Value Have Been Created If Apple’s System Had Been Open?

A recent article in Newsweek, “Think Really Different” by Daniel Lyons, laments the fact that Apple’s ecosystem is a closed system, which represents paradigm shift from the prior, open system the PC industry and post-Internet world had evolved into:

Persuasion technologies include methods and techniques derived from behavioral psychology and behavioral economics used to shape the choices people make. The favorable environment for using such methods, enabled by people’s increasing use of computers and smartphones, has led to the proliferation of their use by software developers.

Like any technology, persuasion technologies can be used for good or evil. However, the increasing dependence of people on digital technologies, together with the increasing prevalence of software developers’ use of persuasion technologies has created emergent behavior in society that’s downright ugly: the emergence of extremism, outrage, and divisiveness among members of society.

This analysis is closely tied to a previous analysis I performed, “Information Distortions on the Internet.”

This analysis will examine

• The nature of persuasive technologies

• The game between software developers and users that has created an environment of good, bad, and ugly

• How the environment might be changed to create more favorable social outcomes

Amazon Ecosystem Components

Amazon Segments

Amazon Sales, Income, and Margins

Amazon Methods of Competition

Anti-Trust Issues

Amazon is leaving a large pile of battered companies in its wake as it increasingly steals sales away from traditional bricks and mortar companies and decimates their market shares. Some of the better-known victims include: Barnes & Noble (books), Macy’s (clothes and home goods), Toys R Us (toys and baby products), Staples and OfficeMax (office supplies), Etsy (handmade products), and Best Buy (electronics).

In fact, there’s even an index, the Bespoke “Death By Amazon” index, that tracks the performance of 54 public companies most at risk from Amazon:

Bespoke publishes the “Death By Amazon” as a way to track performance of the companies most affected by the rise of AMZN. Companies included must be direct retailers with a limited online presence (or core business based on physical retailing locations), a member of either the Re- tail industry of the S&P 1500 Index or a member of the S&P Retail Select Index, and rely on third party brands. We view these attributes as the best expression of AMZN’s threat to traditional retail. The index is designed as both a performance benchmark and idea generation tool for our clients.

On July 17, 2017, Patti Domm reported in “Amazon's victims: These stocks have lost $70 billion so far this year” that the index is down 20% so far this year.

As Amazon’s seeks to dominate yet another market segment – the grocery business – through its plans to purchase Whole Foods, we must question once again whether or not Amazon is “too big” (however we choose to define bigness).

This analysis examines

• The various players in Amazon’s market ecosystem

• The extent to which Amazon covers its ecosystem

• How Amazon earns its money to finance its operation

• The methods Amazon uses to compete

• Other potential anti-trust issues

The Origin of Black Friday

What’s at Stake?

The Evolution of Patterns in Retail Sales

Some Issues Regarding the Black Friday Game

The Origin of Black Friday

The origin of the name Black Friday is described in “Black Friday (shopping) explained” as follows:

The day's name originated in Philadelphia, where it originally was used to describe the heavy and disruptive pedestrian and vehicle traffic which would occur on the day after Thanksgiving. Use of the term started before 1961 and began to see broader use outside Philadelphia around 1975. Later an alternative explanation began to be offered: that "Black Friday" indicates the point at which retailers begin to turn a profit, or are "in the black".

It has only been recently that Black Friday has been the busiest shopping day of the year. Previously, the busiest shopping day of the year had been the Saturday before Christmas. As Miranda Marquit describes it in “What Is Black Friday – History of the Holiday Shopping Phenomenon”

Interestingly, the day after Thanksgiving has only recently become the biggest shopping day of the year. Between 1993 and 2001, it ranked between fifth and tenth on the list of the busiest shopping days. In fact, for years, the busiest shopping day was usually the Saturday before Christmas.

But things changed in 2002. That was the year Black Friday took the lead, and it has remained the busiest shopping day of the year ever since, with the exception of 2004 when it was second.

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

This section on game theory basics of war is relatively brief and non-comprehensive. The purpose of including a discussion of traditional war within an analysis of cyberwar is to provide some basics and points of reference for better understanding the challenges faced in cyberwar.

War forms a game because the actions taken by each interlinked player affect the payoffs to all other players. As such, players must act strategically, taking into account the actions they think other players might take.

Potential Actions

With traditional forms of war, players generally have three options with respect to the actions they may take: (i) They may choose not to arm themselves with weapons, (ii) they may choose to arm themselves in preparation for war against other players, or (iii) given that they have armed themselves, they may choose to attack other players.

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.

Now that we have some understanding of the basics of war – the actions players are able to take, the benefits and costs associated with the different actions, and the crucial variables for determining the stability of the situation – we can move on to cyberwar. The first issue to cover is the definition of cyberwar.

Why Do We Care How Cyberwar Is Defined?

The terms cyberattack and cyberwar have been tossed around in the media, generally without the writers having provided a clear definition of terms. Why do we care about distinguishing cyberattacks from cyberwar and defining exactly what constitutes cyberwar? It is important because there are vital implications for international law and the appropriate use of policy for addressing the actions.

Part of the reason the terms have been used in such a slippery way is that cyberattacks represent a new form of attack that has not been available before now. Furthermore, attempting to frame cyberattacks in terms parallel to those of traditional, real-world attacks has proven to be problematic and not at all clear-cut.

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.