This paper presents a new method for modeling-induced technological learning and uncertainty in energy systems. Three related features are introduced simultaneously: (1) increasing returns to scale for the costs of new technologies; (2) clusters of linked technologies that induce learning depending on their technological "proximity" in addition to the technology relations through the structure (and connections) of the energy system; and (3) uncertain costs of all technologies and energy sources. The energy systems-engineering model MESSAGE developed at IIASA was modified to include these three new features. MESSAGE is a linear programming optimization model. The starting point for this new approach was a global (single-region) energy systems version of the MESSAGE model that includes more than 100 different energy extraction, conversion, transport, distribution and end-use technologies. A new feature is that the future costs of all technologies are uncertain and assumed to be distributed according to the log-normal distribution. These are stylized distribution functions that indirectly reflect the cost distributions of energy technologies in the future based on the analysis of the IIASA energy technology inventory. In addition, the expected value of these cost distributions is assumed to decrease and variance to narrow with the increasing application of new technologies. This means that the process of technological learning is uncertain even as cumulative experience increases. New technologies include, for example, fuel cells, photovoltaic and wind energy conversion technologies. The technologies are related through the structure of energy system in MESSAGE. For example, cheaper wind energy has direct and indirect effects on other technologies that produce electricity up-stream and on electric end-use technologies downstream. In addition, technologies are grouped into clusters that depend on technological "proximity." For example, the costs of all fuel cells for mobile applications are a function of their combined installed capacity weighted according to their expected unit sizes. This relationship depends on how closely the technologies are related. This varying degree of "collective" technological learning for technologies belonging to the same cluster is also uncertain. Each scenario of alternative future developments for a deterministic version of the global energy systems model MESSAGE requires approximately 10 min of CPU time on PC with Intel Pentium II 233 MHz and 128 MB of RAM. Therefore, it is simply infeasible to generate alternative future developments under uncertainty based on a simple Monte-Carlo type of analysis were one sequentially draws observations from the very large number of more than 200,000 cost distributions (100 technologies, 11 time steps, 10 technological clusters with 22 technologies included) assumed here for modeling technological learning and uncertainty. Instead, the new approach proposed here starts with a large but finite number of alternative energy systems "technology dynamics" and generates in "parallel" another large but finite number of deterministic scenarios by sampling from the distributions simultaneously for each of these technology dynamics. In this application, about 130,000 scenarios were generated. There were 520 alternative technology dynamics each with about 250 alternative deterministic scenarios resulting from the simultaneous stochastic samplings. Both numbers were initially varied before deciding that about 500 is a sufficient number of different technology dynamics required for a wide spectrum of alternative technological learning possibilities and that about 250 different deterministic scenarios is a sufficient number to generate most of the interesting future energy systems structures for each of the technology dynamics based on the analysis that in total produced roughly one million different scenarios. These large numbers of scenarios represent a very small subset of all possible ones that is basically infinite. They were not chosen randomly, but are a result of applying adaptive global search technique to the formulated non-convex, non-smooth stochastic problem. From the 520 alternative technology dynamics, about 53 resulted in scenarios with very similar overall energy systems cost. They have fundamentally different technological dynamics and produce a wide range of different emergent energy systems but can be considered to be approximately equivalent with respect to "optimality" criteria (in this case simultaneous cost and risk minimization). Thus, one of the results of the analysis is that different structures of energy system emerge with similar overall costs, i.e., that there is a large diversity across alternative energy technology strategies. The strategies are path dependent and it is not possible to choose a priori "optimal" direction of energy systems development. Another result of the analysis is that the endogenous technology learning with uncertainty and spillover effects have the greatest impact on the emerging structures of energy system during the first few decades of the next century. Over these "intermediate" periods of time these two processes create effective lock-in effects and increasing returns to adoption. In the very long run, however, all of these effects are not of a great importance. The reason is that over such long periods many doubling of capacity of all technologies with inherent leaning occur so that there are few relative cost advantages that result from large investments in some technologies and clusters. Therefore, the main finding is that under uncertainty the near-term investment decisions in new technologies are more important in determining the direction of long-term development of the energy system than are the decisions that are made later, towards the end of the time horizon. Thus, the most dynamic phase in the development of future energy systems occurs during the next few decades. It is during this period that there is a high freedom of choice across future technologies and many of these choices lead to high spillover learning effects for related technologies. One policy implication that can be made based on the emerging dynamics and different directions of energy systems development in this analysis is that future RD&D efforts and investments in new technologies should be distributed across "related" technologies rather than be directed at only one technology from the cluster even if it appears as a "winner." Another implication is that it is better not to spread RD&D efforts and technology investments across a large portfolio of future technologies. It is rather better to focus on (related) technologies that might form technology clusters.