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[2238] a. jafari, a. greenwald, d. gondek, and g. ercal. on no-regret learning, fictitious play, and nash equilibrium. in proc. 18th international conf. on machine learning, pages 226233. morgan kaufmann, san francisco, ca, 2001. [2239] s. jain. learning in the presence of additional information and inaccurate information. phd thesis, university of rochester, 1990. [2240] s. jain. an infinite class of functions identifiable using minimal programs in all kolmogorov numberings. international journal of foundations of computer science, 6 1 ; : 8994, 1995. [2241] s. jain. on a question about learning nearly minimal programs. inform. proc. lett., 53 1 ; : 1 4, 1995. [2242] s. jain. program synthesis in the presence of infinite number of inaccuracies. j. comput. syst. sci., 53 3 ; : 583591, 1996. first appeared in alt '94. [2243] s. jain. strong monotonic and set-driven inductive inference. j. of experimental and theoret. artif. intell., 9 1 ; : 137143, 1997. [2244] s. jain. learning with refutation. j. comput. syst. sci., 57 3 ; : 356365, 1998. first appeared in alt '98. [2245] s. jain. minimal concept identification and reliability. international journal of foundations of computer science, 9 3 ; : 315320, 1998. [2246] s. jain. on a question of nearly minimal identification of functions. inform. process. lett., 70 3 ; : 113117, 1999. [2247] s. jain. robust behaviorally correct learning. inform. comput., 153 2 ; : 238248, 1999. [2248] s. jain. on an open problem in classification of languages. journal of experimental and theoretical artificial intelligence, 13 2 ; : 113118, apr. 2001. [2249] s. jain and e. kinber. intrinsic complexity of learning geometrical concepts from positive data. in 14th annual conference on computational learning theory, colt 2001 and 5th european conference on computational learning theory, eurocolt 2001, amsterdam, the netherlands, july 2001, proceedings, volume 2111 of lecture notes in artificial intelligence, pages 177193. springer, 2001. [2250] s. jain and e. kinber. learning languages from positive data and negative counterexamples. in algorithmic learning theory, 15th international conference, alt 2004, padova, italy, october 2004, proceedings, volume 3244 of lecture notes in artificial intelligence, pages 5468. springer, 2004. [2251] s. jain and e. kinber. learning multiple languages in groups. in algorithmic learning theory, 16th international conference, alt 2005, singapore, october 2005, proceedings, volume 3734 of lecture notes in artificial intelligence, pages 256268. springer, oct. 2005. [2252] s. jain and e. kinber. iterative learning from positive data and negative counterexamples. in algorithmic learning theory, 17th international conference, alt 2006, barcelona, spain, october 2006, proceedings, volume 4264 of lecture notes in artificial intelligence, pages 154168. springer, oct. 2006. [2253] s. jain and e. kinber. learning and extending sublanguages. in algorithmic learning theory, 17th international conference, alt 2006, barcelona, spain, october 2006, proceedings, volume 4264 of lecture notes in artificial intelligence, pages 139153. springer, oct. 2006. 155 and play poker against.
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3.c. economic efficiency15 two types of economic efficiency can be illustrated with this model. technological efficiency refers to production at some point on the ppf. this means that society is getting the most of its scarce resources; no waste occurs. allocative efficiency requires that what is produced is what consumers want. both of these conditions are satisfied when the society produces at the tangency between the highest possible ic and the ppf. in figure 8, society might produce and consume at point a or b; either would be technologically efficient, since they are on the ppf, but not allocatively efficient--these points do not lie on the highest possible ic. point c on ic2 is both technologically and allocatively efficient. of course, we would prefer to produce somewhere on ic3, but society lacks the resources and or technology to make that possible. production and consumption at point c maximizes utility allocative efficiency ; at the lowest possible cost technological efficiency ; . the inefficiency of points a and b relative to point c can also be explained in slightly more technical terms. note that the slope of the ic1 is greater than the slope of the ppf at point a, implying rcsa mrta at that point. put differently, the willingness of consumers to give up autos for computers is greater than the production cost of doing so. the social benefits of producing and consuming fewer autos and more computers outweigh the cost. hence, movement toward c benefits society. on the other hand, at point b, the ppf is steeper than the ic. here the willingness of consumers to sacrifice computers to get more autos is higher than the cost of doing so. overall welfare is improved by moving from b toward c. at point c, the ppf is tangent to ic2. there is no higher ic that still touches society's ppf, so that is the optimal production and consumption point. note that at c, the mrtc mrsc; the willingness to substitute one good for another just equals the production cost of doing so--technological and allocative efficiency and where to play poker.
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