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As already mentioned, many public actors, like private sector actors, are poorly informed and partly driven by self-interest. In addition, there may be interest groups that exert pressure on the political sector to gain (economic) advantages. Powerful and concentrated interest groups, such as large companies, trade unions and industry associations, can use their networks and financial resources to influence policy, often in areas where information is asymmetric, to the detriment of the political sector. These actors can then influence the design of regulations, remuneration systems and tax structures to their advantage. This aspect is further analysed by Holcombe (2024).

Many of the mission failures discussed in our book can be attributed to regulatory capture. Alves (2024) shows how attempts to revive Brazil's shipbuilding industry were influenced by trade unions to provide extensive support to domestic suppliers that were not globally competitive. Waldron and Coyne (2024) argue that foreign aid politicised many receiving sectors in recipient countries, increasing the scope and scale of corruption and rent seeking.

Here, a word of caution. As has been repeated several times in this book, there is no simple and straightforward way of evaluating the prospects or outcomes of an effort to innovate, and hence it is also misguided to try to simply judge the usefulness or profitability of an idea or a project based on the level of productivity of a single entrepreneur and the level of governmental subsidies to that same entrepreneur. It might very well be that in the very long run, subsidized innovation is the most beneficial from overall societal point of view. The problem, highlighted by Gustafsson et al. (2020: 459–460), is the seeming risk of emergence of a class of “subsidy entrepreneurs”, who “find it relatively profitable to engage in grant- seeking activities compared to market production”, and hence undertake “an unproductive form of entrepreneurship”. Analogous to “rent- seeking”, i.e. the systematic attempts by firms to increase their profits not by increasing their productivity but influencing public policy to give out subsidies, imposing tariffs, and the like (Krueger 1974; Helm 2010), such “grant-seeking” diverts resources from core operations including R&D and to applying for subsidies, creating an artificial and self-sustaining economy of subsidies for low productivity.

It should not have to be underscored that public sector bureaucracies are not spared from free-rider problems, nepotism, moral hazard that comes from information advantages of specialist civil servants over elected officials (and electorates), and other similar problems on individual level (Niskanen 1971, 1975). Quite the opposite, it is likely for such challenges to quickly and forcefully present themselves and compromise the theoretically very promising idea that firm subsidies neatly correct market failures and spur innovation in the private sector to the benefit of society as a whole (Gustafsson et al. 2016). There is also much to suggest that several peculiarities and nuisances of representative democracy have been accentuated in the past decades, as elections have come to be decided by the middle ground or the median voter, incentivizing leading politicians both on the right and on the left to focus on the interests of minorities in order to win elections. These minorities may both be very particular in their interests, and may shift rapidly. The result is often less actionable governments and a reduction of political initiative to a wide collection of special interests, more or less compatible with each other, and certainly not conducive of decisive action in urgent policy areas (Helm 2010).

The knowledge problem is but one exponent of what could very well be called “government failure” to counterbalance the hypothesis of “market failure”. Politics is, in and of itself, a struggle between different special interests. These special interests will take every opportunity to benefit from policies that they can have influence over (Niskanen 1975). On a general level, any policy measure will therefore run the risk of being captured by special interests, which means that rather than aiming for utility maximization on overall societal level, it will aim for utility maximization on individual or group level.

Purposive social action always has unintended consequences, changing the behavior of those it is aimed at (Merton 1936) and incentivizing beneficiaries of rewards or subsidies to adapt to terms and conditions rather than aspiring to quality or competitiveness in a broader or general sense (e.g. Muller 2018: 19–20). Direct and indirect subsidies handed out by public sector organizations tend to lead both public and private actors and entities in innovation systems to reorganize their efforts so as to maximize their eligibility for funding, and realign their activities to fit specific aims and purposes. Studies have shown that government subsidies tend to go to those who are good at applying for them, or lobbying for them, rather than those who need them (Karlson et al. 2021: 85). Programs are, allegedly, routinely “hijacked” by special interests so that resources are diverted away from intended goals and to “boosting cronies of the nation’s rulers or legislators” (Lerner 2009: 11). Other studies have demonstrated that entrepreneurs who are productive enough on their own are not only in no need for these types of grants, but indeed abstain from applying at all, and instead use their effort on increasing their productivity (Gustafsson et al. 2020). This seems to imply an inverse correlation between productivity and effort put into acquiring grants.

Schumpeterian = the theory that profit motivates innovation through entrepreneurship

Schumpeterian-Keynesian = the idea that the state can channel this profit into social-welfare enhancing activities

Incentive problems concern whether policies can be designed in a way that is compatible with incentive structures throughout society. Any policy implementation needs to consider the extent to which political decision makers, administrators and commercial forces are driven by incentives that make policies work out as intended. The literature on political economy often highlights public actors’ goal setting as a matter of aligning between different types of interests, including the vote maximizing behavior of politicians and budget maximizing among government agencies. Moreover, politics and public grants in particular are frequently influenced by lobbying of special interest groups at the expense of taxpayers and the public (Niskanen 1975).

Incentive problems help us explain some of the results regarding why public policies for innovation have delivered meagre results. Different public programs tend not to be closed down, even when they have no positive effects, arguably due to the budget maximizing nature of government agencies and the need of politicians to appear as decisive. Government initiatives can also lead to firms developing opportunistic behaviors which, ultimately, can result in both cheating and corruption. A recent German study found that soft loans have not necessarily gone to firms that need them most, but to firms where management have personal connections to funding agencies (Haselmann et al. 2018).

The entrepreneurial state narrative represents a combination of top-down and bottom-up approaches. The call is for states to create markets, radically change the direction of research and development, and then let bottom-up forces work their magic. The example par excellence is the internet, for which radical public sector (defense) research seeded a development that was combined with exponentially increasing entrepreneurial effort to commercialize the new technology and extend it to more areas.

A mission needs to be backed by considerable central power from the very top. The leaders of a large mission will need to subsidize, regulate, and continuously find new top-down paths if the old ones are not working. Each path chosen will eliminate alternative paths, including those that would have followed on those alternative paths, and so on. One reason why economists and proponents of theories of market failure tend to support R&D subsidies is that such a policy works from the bottom up: It does not (at least not necessarily) tell firms what to do or how to do it. When the state is the entrepreneur, grassroot entrepreneurs are not as free to work, and ultimately to spread knowledge, because they are no longer as free to decide the direction of their efforts.

Information problems concern the difficulty a public actor face in collecting the information and acquiring the knowledge enabling correct decision-making regarding, for example, the allocation of resources. As Hayek (1945) showed, it is practically impossible to aggregate information and knowledge about production conditions, business opportunities, customer preferences, etc. to any central unit in society. Such information is dispersed, local, and time-bound in character, even in today’s modern digital economy. With regard to innovation policy and the results reviewed above, there are numerous implications of Hayek’s argument.

First, the existence of a market failure is empirically difficult to prove, or measure. The original argument by Arrow (1962) was of a theoretical nature and has not been validated. One could expect the potential size of such a market failure to vary greatly depending upon institutional characteristics, industrial context, regional and national setting. Such differences along with the fact that it is a very methodologically challenging task to locate and compute the size of a market failure means that policymakers are put in the awkward position of trying to solve a problem that is unknown both in terms of its existence, size and location. Needless to say, such a situation is almost bound to result in malinvestments.

The call for state entrepreneurship is for the state to be not just entrepreneurial, but rather something like the entrepreneur: what Schumpeter (1961) identified as the force that moves the economy toward new equilibria. The difference is that the forces bringing those equilibria about are determined from the top down rather than from the bottom up. The result is a situation in which state entrepreneurship crowds out market-driven entrepreneurship. Indeed, a central tenet of mission-oriented policy is tilting the playing field away from unwanted solutions (Mazzucato, 2016). This process then obviously involves picking winners—an activity for which many of us are not convinced of the state’s expertise.

Researchers who push this narrative do argue that the state must be brave in these endeavors and indeed willing to fail. The problem is that the risk component inherent in the definition of entrepreneurship (Shane & Venkataraman, 2000) is lacking in state entrepreneurship. It is simply difficult to draw the line for what constitutes failure. Failure takes place in two main ways: we can succeed at doing the wrong thing, or we can fail at doing the right thing. The market is a mechanism that distinguishes failure from success in brutal fashion, regardless of the source of the failure. Whether entrepreneurs are unsuccessful at valued ends or successful at unvalued ends does not matter. Whether an entrepreneur produces bad ice cream and falls to competition from other ice cream firms or produces the best typewriters in the world and falls to competitive pressures from the computer industry matters little for the result. The government would need two mechanisms: one for each source of error. When the state fails at the right thing, the main risk is crowding out, in the ordinary sense: firms and technologies that would have better solved the problem are outcompeted. But the biggest long-term threat is that the state succeeds at doing the wrong thing and locks us into a suboptimal equilibrium by tilting the playing field: It crowds out alternative solutions and means altogether. No known method of evaluation could deal with these problems, because the biggest risk is wrapped in a counterfactual.

The case studies in the volume suggest that policymakers rarely have the information needed to design missions effectively and efficiently. For example, in the 1970s cancer research was still relatively undeveloped by today's standards, making it virtually impossible to achieve the goals set by the mission (as pointed out by leading cancer researchers). Lucas and Boudreaux (2024) give another example where politicians continued to spend large sums on a mission related to homelessness that missed its targets early on. This underlines that many of the problems and difficulties associated with– ever so well-intentioned– missions are often discovered only after they have been attempted. Even people with a genuine desire to improve the world have limited knowledge and skills.

In line with this argument, Waldron and Coyne's (2024) contribution is a good illustration of the knowledge problem associated with political governance and missions. Using Mazzucato's principles as a frame of reference, the authors show how difficult it is to succeed in aid-related missions without sufficient information and feedback, which can easily lead to unintended consequences. In the worst-case scenario, missions can do more harm than good. André Alves's (2024) study of the Brazilian government's mission to create a thriving domestic shipbuilding industry provides another illustrative example of a failed mission where the politicians involved did not understand that the domestic industry lacked the necessary competencies and conditions to succeed.

But the real question is whether governmental innovation policy aimed at subsidizing innovation, and balancing market failure, really works. In a fundamental sense, the “knowledge problem” famously identified by Austrian economist Friedrich Hayek (1945) makes the endeavor very challenging, as it puts into doubt whether any uniform policy or intervention, in any society with some level of complexity, can be efficient. The knowledge deficit of any centrally placed actor, in relation to the actors on the field, makes prioritizing difficult. Also a state with the greatest corporativist integration of varieties of special interests will have a disadvantage compared to more dynamic systems (like scientific fields and markets) when it comes to problem choice, problem formulation, and evaluation of possible solutions. Put differently, and somewhat exaggeratedly (in order to clarify the basic argument), no state actor can have an overview of all ideas being pursued by innovators out there that is sufficient to make the choices that are necessary to pick the winners (Sandström and Alm 2022). It has been tried, not without (some partial) success, but with disastrous consequences in broader and long-term perspective. The huge investments in R&D in the Soviet Union were, for the most part, centrally planned and were ultimately motivated by military aims. They produced scientific research of high quality—for one thing, quite a few Nobel Prizes were awarded to Soviet researchers, well into the 1970s—and also some innovation, but in both respects this system was clearly inferior to any country in the West that had a more pluralist and dynamic system. As we will return to in the final chapter of this book, innovation is unpredictable and rather disorganized, and can really only be achieved by fostering pluralism and variation. As summarized by Matt Ridley (2020: 280), “to pretend that government is the main actor in this process, let alone one with directed intentionality, is an essentially creationist approach to an essentially evolutionary phenomenon.”

Lavish arguments aside, the problem of choosing what areas to bet on, and what firms and innovation projects should receive subsidies, is a practical problem for any aspiring entrepreneurial state. Corporate R&D suffers from similar challenges when sizing up and reaching higher levels of complexity, but the limited “ownership competence” (Murtinu et al. 2022) of the state is more problematic given that it operates with resources that are the results of the hard labor of others, which makes wasteful spending particularly wasteful.

Hall = J Storrs Hall, Chief Scientist of Nanorex, Inc, and Research Fellow of the Institute for Molecular Manufacturing

Hall’s indictment of modern innovation policy does not end with regulatory encumbrances. He is equally critical of government research and development (R&D) efforts. “One of the great tragedies of the latter 20th century, and clearly one of the causes of the Great Stagnation,” he argues, “was the increasing centralization and bureaucratization of science and research funding.”

To this point, we have viewed severe psychological harm as a conceptual threshold that can ethically and legally justify a military response. However, psychological distress suffered by civilians may also shape foreign policy decision-making. The pathway comprises two stages. First, psychological harm following cyberattacks shifts public opinion. Second, shifts in public opinion influence national decision-making. This pathway is indirect and offers a secondary perspective to consider the political consequences of cyber operations.

The pathway’s first leg links psychological harm and public policy preferences. Previously, we discussed how the anxiety and fear associated with political violence arouses a newly arisen perception of the world as a malevolent and dangerous place. As a consequence of their threat-driven emotional reorientation, voters adopt certain policy positions to regain a sense of security. When it comes to cyber-threats, recent empirical research verified that public exposure to cyberattacks leads to substantial shifts in political attitudes (Shandler, Snider, and Canetti 2022). Various studies have shown how exposure to cyberattacks corrodes public trust in government institutions (Gross, Canetti, and Vashdi 2016; Shandler and Gomez 2022), and influences support for intrusive surveillance (Snider et al. 2021). It is clear that the emotional distress associated with cyber-attacks generates the conditions for reevaluating core beliefs toward privacy, and that attacks are likely to spawn a realignment of the privacy-security paradigm for the digital age.

When famed Nobel Prize winning physicist Richard Feynmann came up with the concept of quantum computers in 1982 in his speech and paper Simulating Physics with Computers, he probably wasn’t thinking about the effects it would have on cryptography. Today, we face the real possibility of quantum computers overturning the apple-cart of classical encryption and the promise of nigh-unto-unbreakable quantum encryption.

This, not to put too fine a point on it, will change everything.

As should be clear from the above descriptions, for pragmatic backwards compatibility reasons, the foundation of 5G security is exactly as it was in UMTS and LTE (3G and 4G) systems. A 128-bit key K, shared by the USIM and the issuing network, is used as the foundation of all 5G security functions (with the exception of mobile identity confidentiality, which relies on asymmetric cryptography). Clearly this means that if the USIM-resident key K is ever discovered, then the entire basis of 5G security is undermined, despite the fact that 256-bit keys are used in the key derivation hierarchy More specifically, to support the AKA protocol, K is used to help create a 5-value authentication vector, exactly as used in UMTS, including RAND, MAC and two 128-bit keys CK and IK. These values are then used to generate further values, as follows

The value XRES* is computed as a function of XRES, RAND, CK, IK and an identifier from the serving network. In turn HXRES* is then derived from XRES* and RAND. That is, HXRES* is a function solely of RAND, K and the serving network name. The keys CK and IK are then input to a further key derivation function (along with the encrypted SQN) to generate the 256-bit key KAUSF. Next, the 256-bit key KSEAF is derived from a combination of KAUSF and the serving network identifier, and the 256-bit key KAMF is, in turn, derived from KSEAF. Finally, the 128-bit keys actually used for encrypting and integrity-protecting tra c are derived from KAMF (possibly as a multi-step process), initially as 256-bit keys which are then truncated before use. It should thus be clear that the operational keys are a function solely of RAND, K, the serving network name (and certain other public parameters). It should immediately be apparent that there is a major security issue in the PQ era. That is, security is based on a 128-bit secret key, and thus Grovers algorithm (see 2.1) could reduce the effective level of security to that provided by a 64-bit key. Searches of size 264, whilst not trivial, are well within the bounds of what is possible today. Detailed implications for the AKA protocol and operational protection are discussed below. It could be argued that parts of the functions used to generate the values XRES*, HXRES* and the operational keys are issuing-network-proprietary (namely f1 f5). However, this gives very little additional security for two reasons. Firstly `default values for these functions are standardized and are, presumably, in use in some networks. Secondly, all such functions are necessarily built into every USIM, and so a determined adversary could reverse engineer the USIM to learn these functions.

Quantum computer technology is very new and still under development. In this process, different properties can be added to quantum computers according to different usage areas. Quantum computers are not substitutes for conventional computers, and these computers do not provide any advantages for the users apart from their specific use. There is not any extensive study on how quantum computers are used or where they are used. Therefore, in this part of the study, studies on quantum computers and approaches are examined in detail. The summary of the finding are presented in Table 1. The findings revealed the following usage areas:

• Quantum computers have both positive and negative effects for security mechanisms. These effects can be classified as making the public key cryptosystems used in today’s security protocols insecure (SSL, SSH, IPSec), the need for creating reliable cryptosystems instead of classical public key cryptosystems and designing reliable protocols that can work on quantum computer infrastructure.

Genome sequencing has given rise to a new generation of genetically engineered bioweapons carrying the potential to change the nature of modern warfare and defense. Introduction Biological weapons are designed to spread disease among people, plants, and animals through the introduction of toxins and microorganisms such as viruses and bacteria. The method through which a biological weapon is deployed depends on the agent itself, its preparation, its durability, and the route of infection. Attackers may disperse these agents through aerosols or food and water supplies (1). Although bioweapons have been used in war for many centuries, a recent surge in genetic understanding, as well as a rapid growth in computational power, has allowed genetic engineering to play a larger role in the development of new bioweapons. In the bioweapon industry, genetic engineering can be used to manipulate genes to create new pathogenic characteristics aimed at enhancing the efficacy of the weapon through increased survivability, infectivity, virulence, and drug resistance (2). While the positive societal implications of improved biotechnology are apparent, the “black biology” of bioweapon development may be “one of the gravest threats we will face” (2).

Limits of Past Bioweapons

Social manipulation also stands as a danger of artificial intelligence. This fear has become a reality as politicians rely on platforms to promote their viewpoints, with one example being Ferdinand Marcos, Jr., wielding a TikTok troll army to capture the votes of younger Filipinos during the Philippines' 2022 election.

TikTok, which is just one example of a social media platform that relies on AI algorithms, fills a user's feed with content related to previous media they've viewed on the platform. Criticism of the app targets this process and the algorithm's failure to filter out harmful and inaccurate content, raising concerns over TikTok's ability to protect its users from misleading information.

Various forms of AI bias are detrimental too. Speaking to the New York Times, Princeton computer science professor Olga Russakovsky said AI bias goes well beyond gender and race. In addition to data and algorithmic bias (the latter of which can "amplify" the former), AI is developed by humans - and humans are inherently biased.

"A.I. researchers are primarily people who are male, who come from certain racial demographics, who grew up in high socioeconomic areas, primarily people without disabilities," Russakovsky said. "We're a fairly homogeneous population, so it's a challenge to think broadly about world issues."

EK

The rapid advancement of Artificial Intelligence (AI) in the job market has brought forth a myriad of concerns and challenges that cannot be overlooked. While AI undeniably offers benefits, its dark side reveals a host of concerns and problems for businesses and individuals alike. As we delve into the darker implications of AI in the job market, it becomes essential to grasp these challenges and navigate the complex landscape of AI, ultimately working towards effective solutions that mitigate potential negative impacts.

AI-powered job automation is a pressing concern as the technology is adopted in industries like marketing, manufacturing and healthcare. By 2030, tasks that account for up to 30 percent of hours currently being worked in the U.S. economy could be automated - with Black and Hispanic employees left especially vulnerable to the change - according to McKinsey. Goldman Sachs even states 300 million full-time jobs could be lost to AI automation.

"The reason we have a low unemployment rate, which doesn't actually capture people that aren't looking for work, is largely that lower-wage service sector jobs have been pretty robustly created by this economy," futurist Martin Ford told Built In. With AI on the rise, though, "I don't think that's going to continue."