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We address the problem of detecting deviations of a binary sequence from randomness, which is very important for ra ndom number (RNA) and pseudorandom number generators (PRNG) and their applications to cryptography. Namely, we consider a hypothesis that a given bit sequence is generated by the Bernoulli source with equal probabilities of 0’s and 1’s and the alternative hypothesis that the sequence is generated by a stationary and ergodic source which differs from the source under . We show that data compression methods can be used as a basis for such testing and describe two new tests for randomness, which are based on ideas of universal coding. Known statistical tests and suggested ones are applied for testing PRNGs which are used in practice. The experiments show that the power of the new tests is greater than of many known algorithms.

When static models of the firm are considered regulators make serious errors in the determination of the proper wholesale price as the opportunity cost of the delay option is neglected. For an incumbent, the option is exercised and represents an opportunity cost. For a potential entrant the delay option need not be exercised should the regulator allow the purchase access at below economic cost, i.e., operating cost plus the delay options cost. Thus service-based entry is excessive and facilities-based entry remains suboptimal. To summarize, in a static-regulated paradigm: wholesale prices are below their economic cost; inefficient service entry occurs as prices are not set at the correct marginal cost while an entrant is not required to exercise a delay option; social welfare is suboptimal; incumbent firm’s valuation by the financial markets is less; and the cost of capital for incumbent firms is higher. When a regulated paradigm is dynamic the converse holds: optimal prices are higher than for static calculations; only efficient service entry occurs as prices are set at the correct economic marginal cost; facilities-based entrants receive correct price signals; social welfare is maximal; an incumbent’s valuation by financial markets is higher; and the cost of capital is lower than for a regulated paradigm. This chapter has demonstrated the profound implications for regulatory practices. In particular, dynamics and uncertainty matter for the realization of optimal regulatory pricing outcomes. However, much work remains to develop and quantify precise models to fit the telecommunications context.

This chapter attempts to provide some useful insights into the understanding of fundamental tradeoffs faced by B&M and Virtual firms competing in Virtual marketplaces for Type-2 customers. An innovation contained in the paper is the establishment of a link between firm relative locations within the Virtual market to investment and ultimately profits. This innovation enabled the derivation of some testable predictions. Some ways to move beyond current model assumptions could be the consideration of non-uniform distributions of Virtual consumers (in particular non-symmetric distributions due to the skewed age distribution of customers making purchases online), the introduction of market power and allowing both the B&M and Virtual markets to be made open to competition.

When static models of the firm are considered regulators make serious errors in the determination of the proper wholesale price as the opportunity cost of the delay option is neglected. For an incumbent, the option is exercised and represents an opportunity cost. For a potential entrant the delay option need not be exercised should the regulator allow the purchase access at below economic cost, i.e., operating cost plus the delay options cost. Thus service-based entry is excessive and facilities-based entry remains suboptimal. To summarize, in a static-regulated paradigm: wholesale prices are below their economic cost; inefficient service entry occurs as prices are not set at the correct marginal cost while an entrant is not required to exercise a delay option; social welfare is suboptimal; incumbent firm’s valuation by the financial markets is less; and the cost of capital for incumbent firms is higher. When a regulated paradigm is dynamic the converse holds: optimal prices are higher than for static calculations; only efficient service entry occurs as prices are set at the correct economic marginal cost; facilities-based entrants receive correct price signals; social welfare is maximal; an incumbent’s valuation by financial markets is higher; and the cost of capital is lower than for a regulated paradigm. This chapter has demonstrated the profound implications for regulatory practices. In particular, dynamics and uncertainty matter for the realization of optimal regulatory pricing outcomes. However, much work remains to develop and quantify precise models to fit the telecommunications context.

This chapter considered a Hotelling model that incorporates competition effects arising from offline firm entry into online channels by examining equilibrium outcome when offline firms compete in online markets. Comparison is made of the welfare loss from online channel entry. First, a symmetric case of two offline firms demonstrates that, compared to the case of pure online competition, the entry into an offline channel may reduce welfare depending on the magnitude of consumer’s transport costs relative to firm’s offline delivery costs. The analysis is extended to an asymmetric case where two offline firms supply different quality goods. The result for this case provides useful input for policymakers dealing with online markets. Further research needs to consider dynamic issues such as the impact of network effects of online business and the lock-in effect of offline channels. Other challenging issues that require examination are the strategic incentives of hybrid firms, e.g., a storage cost saving effect from multiple channels and a complementary effect of online channels from advertising and online experience.

This chapter attempts to provide some useful insights into the understanding of fundamental tradeoffs faced by B&M and Virtual firms competing in Virtual marketplaces for Type-2 customers. An innovation contained in the paper is the establishment of a link between firm relative locations within the Virtual market to investment and ultimately profits. This innovation enabled the derivation of some testable predictions. Some ways to move beyond current model assumptions could be the consideration of non-uniform distributions of Virtual consumers (in particular non-symmetric distributions due to the skewed age distribution of customers making purchases online), the introduction of market power and allowing both the B&M and Virtual markets to be made open to competition.

When static models of the firm are considered regulators make serious errors in the determination of the proper wholesale price as the opportunity cost of the delay option is neglected. For an incumbent, the option is exercised and represents an opportunity cost. For a potential entrant the delay option need not be exercised should the regulator allow the purchase access at below economic cost, i.e., operating cost plus the delay options cost. Thus service-based entry is excessive and facilities-based entry remains suboptimal. To summarize, in a static-regulated paradigm: wholesale prices are below their economic cost; inefficient service entry occurs as prices are not set at the correct marginal cost while an entrant is not required to exercise a delay option; social welfare is suboptimal; incumbent firm’s valuation by the financial markets is less; and the cost of capital for incumbent firms is higher. When a regulated paradigm is dynamic the converse holds: optimal prices are higher than for static calculations; only efficient service entry occurs as prices are set at the correct economic marginal cost; facilities-based entrants receive correct price signals; social welfare is maximal; an incumbent’s valuation by financial markets is higher; and the cost of capital is lower than for a regulated paradigm. This chapter has demonstrated the profound implications for regulatory practices. In particular, dynamics and uncertainty matter for the realization of optimal regulatory pricing outcomes. However, much work remains to develop and quantify precise models to fit the telecommunications context.

This chapter attempts to provide some useful insights into the understanding of fundamental tradeoffs faced by B&M and Virtual firms competing in Virtual marketplaces for Type-2 customers. An innovation contained in the paper is the establishment of a link between firm relative locations within the Virtual market to investment and ultimately profits. This innovation enabled the derivation of some testable predictions. Some ways to move beyond current model assumptions could be the consideration of non-uniform distributions of Virtual consumers (in particular non-symmetric distributions due to the skewed age distribution of customers making purchases online), the introduction of market power and allowing both the B&M and Virtual markets to be made open to competition.

When static models of the firm are considered regulators make serious errors in the determination of the proper wholesale price as the opportunity cost of the delay option is neglected. For an incumbent, the option is exercised and represents an opportunity cost. For a potential entrant the delay option need not be exercised should the regulator allow the purchase access at below economic cost, i.e., operating cost plus the delay options cost. Thus service-based entry is excessive and facilities-based entry remains suboptimal. To summarize, in a static-regulated paradigm: wholesale prices are below their economic cost; inefficient service entry occurs as prices are not set at the correct marginal cost while an entrant is not required to exercise a delay option; social welfare is suboptimal; incumbent firm’s valuation by the financial markets is less; and the cost of capital for incumbent firms is higher. When a regulated paradigm is dynamic the converse holds: optimal prices are higher than for static calculations; only efficient service entry occurs as prices are set at the correct economic marginal cost; facilities-based entrants receive correct price signals; social welfare is maximal; an incumbent’s valuation by financial markets is higher; and the cost of capital is lower than for a regulated paradigm. This chapter has demonstrated the profound implications for regulatory practices. In particular, dynamics and uncertainty matter for the realization of optimal regulatory pricing outcomes. However, much work remains to develop and quantify precise models to fit the telecommunications context.

When static models of the firm are considered regulators make serious errors in the determination of the proper wholesale price as the opportunity cost of the delay option is neglected. For an incumbent, the option is exercised and represents an opportunity cost. For a potential entrant the delay option need not be exercised should the regulator allow the purchase access at below economic cost, i.e., operating cost plus the delay options cost. Thus service-based entry is excessive and facilities-based entry remains suboptimal. To summarize, in a static-regulated paradigm: wholesale prices are below their economic cost; inefficient service entry occurs as prices are not set at the correct marginal cost while an entrant is not required to exercise a delay option; social welfare is suboptimal; incumbent firm’s valuation by the financial markets is less; and the cost of capital for incumbent firms is higher. When a regulated paradigm is dynamic the converse holds: optimal prices are higher than for static calculations; only efficient service entry occurs as prices are set at the correct economic marginal cost; facilities-based entrants receive correct price signals; social welfare is maximal; an incumbent’s valuation by financial markets is higher; and the cost of capital is lower than for a regulated paradigm. This chapter has demonstrated the profound implications for regulatory practices. In particular, dynamics and uncertainty matter for the realization of optimal regulatory pricing outcomes. However, much work remains to develop and quantify precise models to fit the telecommunications context.