Automating away the centre? Optimal planning and the menace of bureaucratisation

Introduction

‘The silliest thing to do is not to calculate. The second most silly thing to do is to follow blindly the results of your calculation’ – Michal Kalecki

Since the conception of the discipline, economics has been understood as the rational means of identifying the best use of scarce resources between competing ends, regardless of whether it is the survival of Robinson, the prosperity of one’s family, the wealth of nations, an egalitarian harmony or simply the generation of individual profits. Without a doubt, the means and ends discovered in this process, formerly referred to as ‘political economy’, can differ fundamentally. History nevertheless reveals to us surprising overlaps of techniques that cross the lines of opposing economic systems, which provide some empirical proof for the universal character or an essential relatedness of economic problems including possible solutions, or at least unveil a specific type of economic rationalisation that is at play across different cultures – in case one rather prefers a more constructivist viewpoint.

One such method is mathematical optimisation. Today all successful companies deploy some form of optimisation algorithm in intra-planning processes, whether it is for supply chain scheduling, to determine warehouse layouts, cargo fleet routing, or for the optimal use of machinery. But can such a technology, which has proven its efficacy on the enterprise level, be applied to work in the interest of workers within a post-capitalist society collectively planning production? For many critical observers, such algorithms are lumped together with other algorithmic management technologies and seem merely to be the cause of alienation by intensifying labour. However, an economic system that wishes to reduce working hours and avert ecological collapse by reducing resource consumption, through means other than austerity, would also have to deal with such issues as the optimal use of resources. This is also why the coordinative challenge of a socialist mode of production remains to transcend the allocation through the price mechanism with a more rational form of distributing scarce resources. At stake is whether mathematical approaches can assist in the coordination of material flows to realise a post-capitalist future. ¹

To better understand this fundamental challenge and to grasp why optimisation algorithms are even discussed today it might be helpful to conceptualise the economy to be in search of allocative efficiency in order to realise the greatest possible satisfaction of needs. This search space is of course restricted by natural and social constraints and has a dynamic and static dimension to it. Despite their practical entanglement, one can here think of dynamics as dealing much more with the uncertainty of future changes, while statics relate more to the present state of the economy. A short example can better illustrate this. Say a given economy has the capacity to produce a million units of a certain microprocessor, but total demand exceeds this quantity. At this point, the dynamic planning decision could be made to increase supply or develop a new type of chip, both requiring investment in new capital stock such as lithography machines. If one wishes to hand over such investment decisions neither to the market and financial speculation nor to bureaucrats but to socialise the investment function then this economy would require what Aaron Benanav (2020) calls democratic ‘planning protocols’, or what Maxi Nieto (2021) refers to as ‘investment councils’. Yet, to implement this, it will still require allocative decisions in the present that in many cases will take place under scarcity. Required resources for a new semiconductor fabrication plant might also be demanded elsewhere in the economy and limiting factors will likely not allow to satisfy all productive demands. ² The same applies to our processors. Until production is ramped up to meet demand, one would face a state of shortage, requiring again decisions on where this scarce good is best allocated. While in their opposition to finance capital, there seems to be a rough consensus among socialists on the dynamic aspects of planning towards the democratisation of the investment function, the widest dissent can be encountered in solving this static allocation problem, which comes down to the question of who gets what in the economy at a given point in time. ³

So far, our imaginative space seems to be constrained to five general trajectories of dealing with the static aspect of allocative efficiency. The first trajectory for solving this problem is the status quo of the market order. It is the price mechanism still present in market socialist proposals, which steers the flow of scarce resources into the direction of the most profitable enterprises. As a point of departure, this is what socialists still dedicated to the idea of an economic plan wish to transcend. Second, following on from Soviet command planning, these allocative decisions could be taken up again by bureaucrats. Marx disparagingly called this the ‘papacy of production’, and – after the experience of actually existing socialism – any such return to economic despotism seems to present the least desirable alternative. Third, as an inversion of such bureaucratic centralism, one could agree to grant the immediate producers full control over the allocation of the products of their labour, which would amount to an equally irrational dictatorship of the periphery, or an anarchism without markets, due to their particular interests and limited scope of societal needs. A fourth trajectory, favoured by most anti-authoritarians today, would be to solve this problem through a deliberative negotiation process involving everyone affected to reach consensus. Based on voluntarist principles, it is the utopian belief that the allocative efficiency of a world economy would harmoniously emanate from the multitude. In a slightly more bureaucratic form, this hope is articulated today in Pat Devine’s (1988) call for negotiated coordination. Even in the unlikely case that consensus on the allocation of millions of scarce goods could be achieved among the participants ⁴ by horizontal means with a myriad of meetings taking place simultaneously, each individual allocation decision would have cascading effects within the economy resulting in a recursive loop of permanent renegotiation, which would paralyse the economy in an inconclusive state and reinforce desperate calls for a rationing by price or bureaucratic hierarchies to resolve persistent conflicts. Finally, the fifth and last resort humanity seems to have for coming to terms with the complexity of this allocative task is some form of algorithmic mediation.

In an attempt to provide some answers regarding the emancipatory potential of such algorithmic coordination, the following paper begins with a first part providing a historical introduction to optimal planning, while also briefly sketching classical Soviet command planning and its shortcomings. The second part engages with more recent proposals to apply optimisation algorithms to resolve the socialist inquiry for allocative efficiency and will outline four fundamental problems optimal planners would face, namely: the concern of computational complexity, the challenge of generating the economic data necessary for the constitution of such a system, the issue of reconciling a static optimum with the dynamism of real economies, and ultimately the relation of optimisation solvers to a hierarchy of ends, questioning whether such an optimal planning framework might be able to surmount both the economic and political problems of the Soviet system.

The Soviet system

Classical Soviet command planning ⁸ consisted of three levels: at the top reigned Gosplan, responsible for implementing the political goals of the Politburo regarding plan targets for key industries and ensuring the consistency of the national production plan for highly aggregated key goods. Immediately below Gosplan were the industrial ministries, in charge of linking centre and periphery by breaking down the aggregated instructions that were passed down by the central planning commission to distribute the output target to the branches of industry under their auspices, and for aggregating the reports by the enterprises on the way up. At the bottom were the individual enterprises responsible for the fulfilment of the obligatory plan targets. In certain periods, different intermediate bodies – from Sovnarkhozy to associations (see Gorlin, 1974) – were called into existence, which were located between the enterprises and ministries in the planning hierarchy.

Despite the hierarchical nature of this planning process, it is Gosplan that drafted the national production and distribution plan while ensuring the material balance between inputs and outputs, enterprises could still influence the plan by sending counter-proposals up the chain of command. With the centre digesting this information provided from below, the planning procedure would then go through several rounds of iteration. Yet, either way the enterprises were subject to the binding instructions of planning bureaucrats and had little scope for self-direction, as their superiors held sway over both what they should produce and with what materials they should achieve their production targets, as producers also did not have much influence over choosing their suppliers. Led by party-appointed managers, the internal organisation of the workplace mirrored the pyramidal form of the Soviet planning apparatus. Democracy at the workplace or within the wider economy was de facto non-existent.

While competitive structure of the market tends to overproduction, resulting in a buyer’s market, where customers are ideally in the beneficial situation to choose between suppliers, the Soviet economy was an economy of shortage (Kornai, 1980), a dictatorship of the supplier, where customers only had the Hobson’s Choice to take what they were allocated or leave it. Although there were no official horizontal links between enterprises without the mediation of bureaucratic superiors, the result of chronic misallocations was that an informal grey zone emerged, tolerated by the officials, where enterprise supply agents haggled directly with each other using the allocated resources they had no use for to secure the necessary inputs for their production unit. In this system, most economic calculations and allocations of resources were realised predominantly in physical terms. Although prices existed, they were determined not through market forces but by the central authorities. Thus, value indices had less of an allocative purpose rather than a ‘control and evaluation function’ (Bornstein, 1962: 66). Yet, even the latter function had hardly any consequences for the wider economy, as the inefficiency of the system was further intensified due to a lack of any mechanisms for weeding out unproductive or superfluous producers through bankruptcy: Soviet enterprises were running with a ‘soft budget constraint’, which meant that individual enterprise losses were ‘paid by some other institution, typically by the State’ (Kornai, 1986: 4) and the responsible managers usually retained their positions regardless of performance.

The systemic problem of shortages was only exacerbated by the endemic overinvestment inherent to the Soviet economy, an investment hunger that was caused by the overambitious plan targets of the political leadership, the unsustainable prioritisation of military and industry over consumer needs, as well as the empire-building of sectorial and regional ministries, which all led to the fragmentation of investments and long delays in the finalisation of projects. This critical defect of chronic shortages not only had quantitative effects such as unplanned downtime in manufacture due to ‘unproductive slack’ (Kornai, 1980: 103) but also reduced the overall quality of available goods. There were hardly any consequences for producers for providing inferior quality goods or products with specifications for which there was no demand. Furthermore, long-term thinking was disincentivised, and what mattered instead was the short-termist fulfilment of quarterly plan targets. Overall consumer feedback was highly dysfunctional in the Soviet Union, particularly for end consumers. A production seemingly detached from consumer needs together with a dysfunctional pricing system led to an economic reality of large stockpiles of overpriced goods and long queues with black markets when they were under-priced.

Another serious problem of the system was that the centre was not able to process disaggregated information, thus requiring the mid-level ministries as an interface. One of the better-known consequences of this were the aggregated target indicators that were passed down to the enterprises, which created perverse incentives. In the absence of functional feedback channels and without further specifications, physical output targets created curious distortions from consumer needs due to the strategic manipulation of factory managers at gaming the system. Among countless such stories, a popular anecdote is the manufacture of Soviet nails, which were either tiny in size when the plan target was expressed by a certain quantity of pieces demanded or enormous when it was articulated in tons. Another incentive problem was that producers disguised their productive capacities to easier fulfil their plan targets, and also inflated their input requirements to ensure that they were allocated the necessary amounts. The overall supply uncertainty for producers led to further inefficiencies such as the hoarding of large stockpiles as well as the in-house production of critical components, leading to less specialised factories and a decrease in overall efficiency.

Ultimately, Soviet planning was built on the idea of material balances to ensure plan consistency. For a plan to be ‘feasible’ meant merely, first, that the resources used in the plan were actually available, and second, that the output of suppliers needed to match the input of other producers, that is, consistency of input and output throughout the economy. In theory, this was achieved with material balance sheets, yet in practice the centre was overwhelmed by the task, because the data provided was not very reliable, it lacked the crucial information of the availability of resources, and the central planners were often lagging behind, unable to process the available data fast enough due to its bureaucratic limitations grounded in the overwhelmingly analogue state of the planning infrastructure. This is why the central organs had to formulate their plans using coarse categories, which led to troublesome aggregation errors: While the national production and distribution plan might be balanced in aggregate terms, it was inconsistent on the disaggregated level, so that the plan had to be amended regularly to correct the consequential errors throughout the year. But even if the centre could have been able to formulate a feasible plan in disaggregated terms for the national economy by means of material balances, this would not have allowed them to determine whether it might be a good or even the best one. It is in this environment, wherein certain Soviet scientists hoped to enhance this planning process through mathematical optimisation methods, sometimes also referred to as ‘economic cybernetics’ or ‘planometrics’ (Zauberman, 1962), whose application Kantorovich framed the following:

the system of optimal planning by no means presupposes the full centralisation of economic decisions. On the contrary, thanks to the fact that together with the national economic plan a system of prices and valuations (output/capital norms, rent for land and natural resources, investment efficiency norms and so on) consistent with it is compiled, the possibility arises of taking decisions maximally consistent with the interests of the national economy locally. This is conducive to the wide utilisation of the initiatives of economic collectives, the possibility of mobilising resources and uncovering reserves locally, allows the expansion of the rights of separate economic units, and the construction of a system of valuations and stimulation of the work of separate units, such that, that which is profitable for society as a whole is profitable also for every enterprise. In other words, such a system creates the theoretical basis for the solution of the problem of the combination of centralized management of the economy with wide rights and initiative locally on the basis of economic methods of control. (As quoted by Ellman, 1968: 117)

While mathematical programming would necessarily take on a central role in this procedure its advocates well understood that such algorithms would not solve all of the problems mentioned above and would instead have to be introduced alongside market mechanisms and other reforms that democratise the planning process. So despite the perceived technocratic rule of applied mathematics over economic matters, these planning theorists in fact believed that their approach would ultimately allow for an increase in the autonomy of the periphery.

This discursive upheaval of optimal planning from operations research on the enterprise level emerged in the wake of the Soviet cybernetic movement, which provided a wider philosophical language but also a material push towards the networking of the country. Cybernetics, which had initially been shunned as a reactionary pseudoscience by Stalin, was rehabilitated after his death through influential figures such as cybernetician pioneer Anatoly Kitov and soon became state doctrine under Nikita Khrushchev’s vision of automating the economy. Yet, ambitions to network the planning apparatus through information-technological infrastructure (hardware) and to apply mathematical models as well as necessary reforms (software) on the national level ultimately failed due to the lack of political will. There was a surge in the popularity of economic cybernetics from the late 1950s to the early 1970s spearheaded by the ‘System for Optimal Functioning Economy’ (SOFE) research program led by Nikolai Federenko and Vasily Nemchinov, including several attempts to build national information networks, most notably the ‘All State Automated System’ (OGAS) envisioned by cybernetician Victor Glushkov. ⁹ However, all of these attempts were met with heavy resistance from both market reformers as well conservative forces amongst self-interested administrators committed to the status quo, who well understood that a successful introduction of such technology will result in a loss of their power or might even make them obsolete. Many local projects by ambitious optimal planners failed because the ministries simply did not provide the relevant statistical data. Already in 1961 Polish economist Oskar Lange would compare the situation of the Soviet planometrician to ‘a chemist who is denied access to his laboratory or an astronomer prevented from observing the sky’ (As quoted in Smolinski, 1973: 1190). Another important factor influencing why optimal planning was never implemented on a larger scale in the Soviet Union was the justified scepticism concerning the viability of the available computational power at the time, linked to the astronomical investment costs attached to realising this vision. The risk of failure was simply too high, and liberal market reforms came with the promise of being the less risky option.

The widest application of optimal planning in the Soviet Union was realised by Kantorovich himself in 1970, in optimising the production scheduling of the steel industry, addressing about ‘1,000,000 orders, involving 60,000 users, more than 500 producers and tens of thousands of products’. After collecting the necessary data for six years, Kantorovich formulated the optimisation of production schedules and attachment plans as a linear program with ‘more than a million unknowns and 30,000 constraints’ with actual savings in steel after implementation of ‘only 108,000 tons, although the calculated saving was 200,000 tons’ due to imperfect information. Nevertheless, it turned out

that the use of computers in planning the steel industry had a major advantage in addition to enlarging output by making better use of productive capacity. It enabled the degree of aggregation of requirements during the planning process to be reduced, and hence reduced the divergence between output and requirements (Ellman, 1973: 72–75).

Prior to the collapse of the Soviet Union there have been a variety of such projects, yet hardly any of their advocates have gone so far as to optimise the economy within a single model, a hope that was ‘dubbed computomania by its opponents’ (Gardner, 1990: 646). János Kornai, for instance, who developed and applied methods of optimal planning on behalf of Hungary’s socialist government and gradually became disillusioned with the prospect of efficient economic planning through his experience in the trenches of the Soviet-style planning system, stated already in 1970 that it is ‘a science-fiction idea to cover all relevant problems of an economic system in a single model’ due to the sheer number of variables and equations, calling instead for ‘a united system of models’ (Kornai, 1970: 14–15). ¹⁰ Similarly, in a last gasp before he died in 1986, also for Leonid Kantorovich the final goal of his vision remained not the formalisation of a single model but the creation of

a single complex of interconnected models encompassing the entire national economy, as well as systems of forecasting, planning, and centralized and decentralized management of the national economy that provide working people and managers of individual levels and sectors of the economy broad opportunities to display their initiative. (Kantorovich et al., 1987: 17)

In addition to the complexity and enormity of the problem in terms of the amount of variables that speak against a singular model, it also proved difficult to derive an objective function for the economy as a whole. Maximising the output of a plywood trust in the right proportions or minimising the material consumption in the steel industry is one thing, but by which means should the entire economy be optimised? What would be the criterion of such optimality? There has been an intense debate on this question in the Soviet literature, which Alec Nove summarises in the following terms:

The party’s general economic policy objectives are too general, too diffuse, to serve as an operational criterion. If asked by the leadership to produce a programme which contains optimal targets for the year 2000, the economic profession cannot escape the problem by taking the targets adopted by the leadership as its optimisation criterion. After all, the leadership asks the advice of the economic profession about what these targets should be! How is one to distinguish means from ends? Various proposals are mooted: maximise the national income; maximise labour productivity; minimise costs for a given set of outputs; and so on. To maximise human welfare is altogether too vague, and the more intelligent Soviet specialists never forget that some aspects of welfare (leisure, quality of life, environment, etc.) do not figure in national income statistics. But in the Soviet case even the purely material part of welfare is poorly reflected in aggregate statistics. […] The whole notion of somebody defining an objective function rests on the incorrect assumption that there is only one actor. (Nove, 1991: 101)

Initiated by the Liberman Reforms in 1965, the victory of decentralising market reforms was deemed complete when Gorbachev’s perestroika put the final nail in the coffin of any attempt at nationwide algorithmic coordination. And with the ensuing collapse of the Soviet Union, further research on optimal planning came to an abrupt halt. Yet, as it was never fully implemented in the Soviet Union the question remains open as to whether the assessment would be different with contemporary technology at hand. But are our technical means today sufficient to allow the centre to plan in disaggregated terms and to realise a mathematically optimised production and distribution plan, and if so, would this solve the shortcomings of the Soviet system mentioned above, especially in regard to consumer satisfaction and workers’ autonomy?

The return of optimal planning and its computational complexity

While many planometricians like János Kornai turned their back on optimal planning after the implosion of the Soviet Union, the torch was subsequently picked up by the Western Marxists Paul Cockshott and Allin Cottrell in their influential book Towards a New Socialism published in 1993, where they developed a cybernetic planning model that supplants classical linear programming with a more efficient ‘harmony algorithm’, which laid the groundwork for the discursive resurgence of using algorithms for the economic coordination of material flows. In its simplicity, this rather mechanistic framing of the economy envisions the socialist allocation problem to be solved within a unified model consisting of a set of gigantic input-output matrices in disaggregated terms providing information about production methods in relation to a target vector, that is the plan target for final consumer goods. What is eventually optimised in the model is plan fulfilment, or in other words, the harmony of supply and demand. This is achieved mathematically through a singular objective function (i.e. the harmony algorithm), subject to a set of constraints, that minimises the deviation of the final production output from the initial plan target for final consumer goods. As an outcome of this optimisation process, one receives a detailed allocation plan for the entire economy. ¹¹ Their decision for a single objective function is built on the assumption that contemporary information technology would allow the process to run in disaggregated terms, which constitutes a crucial difference in comparison to the situation with poor aggregate statistics Soviet planners faced. In theory, this would allow for detailed allocation decisions to no longer be undertaken by bureaucrats endowed with particular interests, but by a seemingly objective algorithm. In this way, the motivation to use such algorithms is not only to increase allocative efficiency, but despite its centralist characteristics also to wrest the political power formerly held by Soviet bureaucrats and transcend the arbitrariness of their subjective decisions to a more objective process guided by formal logic.

More recently, other proposals were put forward applying the simplex method (Dapprich, 2022), machine learning (Samothrakis, 2021) or interior point algorithm (Härdin, 2021b) for their optimisation. ¹² Regarding their technical feasibility, such algorithmic calculations rest upon a twofold challenge. In the literature this is usually understood as the two parts of Friedrich Hayek’s ‘knowledge problem’, which can be subdivided into the issue of generating and that of processing economic data. ¹³ While usually attributed to Hayek, the latter concern was already articulated by his colleague Lionel Robbins in 1934, who acknowledged that the socialist planning problem could be framed as a system of equations, but believed that

in practice this solution is quite unworkable. It would necessitate the drawing up of millions of equations on the basis of millions of statistical tables based on many more millions of individual computations. By the time the equations were solved, the information on which they were based would have become obsolete and they would need to be calculated anew. (Robbins, 1934: 151)

Meanwhile, regarding classical linear programming schemes, this argument still holds true today, due to their rather high computational complexity. This run time of algorithms is differentiated in computer science in complexity classes according to the big O notation, with the most important classes being: constant O (1), linear O (n), log-linear O (n log n), polynomial O (n^k) or exponential O (eⁿ). Given the same input size (n) the run time of these classes differs substantially. Algorithms that are within exponential time are only feasible for the tiniest data sets, but also those with polynomial run time can bring contemporary supercomputers to their limits if the input is large enough. For a long time, it was not known whether linear programming techniques such as the simplex method had an exponential complexity. Today we know that for most problems the simplex algorithm’s complexity is within polynomial time, with a complexity of about n³. It is certain that during the 70s and 80s, not enough computing power could have been mobilised in the Soviet Union to handle an input of millions of variables, ¹⁴ and even with the computational means of the present, an algorithm of that complexity would be practically infeasible for calculating a plan for a national economy in disaggregated terms.

However, the recent proposals build on the insight that in dynamic economies there will be no need for an optimal solution, and approximations will likely be sufficient for the task. By sacrificing a degree of optimality for computational feasibility, algorithms like Cockshott’s harmony algorithm, which is of order O (n log n) (Cockshott, 2019b: 314), and also the interior point method used by Härdin (2021b), which allows for input sizes up to several billions of variables to be computed in a matter of hours on a single machine, render the computational part of the knowledge problem null. Computation time is further reduced by the fact that the plan does not have to be recalculated in its entirety, as operations will only have to be performed on variables that have changed between iterations, and that such a matrix would be very sparse, because the overwhelming majority of the entries in such a disaggregated input-output table will be null since only a fraction of all existing products will ever enter as an input into the production of another good. ¹⁵ Distributed computing would allow for these calculations not to be made in a single computation centre but across a decentralised computer network.

Thus, it seems that the technical problem of processing the economic data appears to be tractable, although the authors of a recent Austrian critique informed by complexity economics and Hayek’s theory of mind claim otherwise, arguing that the ‘complete control of any complex system is impossible, due to the problem of self-reference’ (Moreno-Casas et al., 2022: 574–575). However, such a framing of computational complexity goes far beyond the technical issue of number crunching, which seems to be – and here even the Austrians might also agree – not the critical limitation of such an economic framework. What is at stake is not whether our technical systems can handle the data load, but how accurate economic data is generated and fed into such a model, as well as how to then lay the static map back over the dynamic territory.

Dynamics, uncertainty and social domination

Similar to the approximation of non-linearities with linear functions, a static optimum would have to constantly be recalculated to approximate the dynamism of the economy. What would be the interval for such adjustments? Would it be necessary to go down to hours, minutes, or even seconds? Here exists a fundamental contradiction between the necessity to manually adjust production functions taking place under a human time horizon and the need for permanent plan revisions calculated with algorithmic velocity. Humans cannot keep up with shorter time intervals but longer ones will introduce erroneous signals and dreadful lag into the economy. At question is also whether a mere contraction of adjustment intervals would truly suffice to deal with the probabilistic nature of real economies and the uncertainty unknowns infuse into the system. It appears to be that economic motion is simulated here by stringing together momentary and shaky snapshots that would to a degree always remain speculative. ²²

This fundamental challenge of integrating the dimension of time back into their calculations is another serious problem such an optimal planning framework would have to face, which will lead to several consequential problems concerned with the temporality of actual allocation. ²³ As part of the problems that arise when relating the static map back to the dynamic territory, the application of the model, the enforcing act of imposing it on the real world, could therefore be understood as the third aspect of the knowledge problem. This should become evident when thinking about the deviation between model and material reality. What the optimisation solvers are calculating is a one-year plan that will never be truly optimal due to inaccurate economic information. But what might it mean for allocative decisions on the ground when in the face of the dynamism of the economy a new one-year plan has to be calculated every other moment? If there were to be some economic rhythm in the form of a year-long planning period, then what would already have been allocated during the elapsed time must have to be taken into account, possibly by subtracting it from the newly calculated yearly allocation.

Although we might no longer run into aggregation errors, we still have to deal with the problem of what one might call the ‘temporal breakdown of allocations’. For the calculation of an optimal allocation it would be necessary to determine at what point in time certain quantities of specific goods will be available for distribution. This is also linked to the essential question of the order in which the different producers benefiting from the yearly allocation will be delivered. In other words: Who gets their resources and components first? This seems to scream for some coordinative entity that will have to make such allocative decisions, weighing the risk of shutdowns between different industries and enterprises against each other, if this information cannot be integrated into the model. While most optimal planners like Cockshott or Dapprich simply disregard this critical issue of time, the only solutions that have been brought up so far to mitigate this problem is to further differentiate products ‘with respect to the time period during which they are produced’ (Kantorovich, 1976: 26), which significantly increases the number of variables, and by reducing the planning horizon from a year to shorter time periods as advanced by Dave Zachariah and Loke Hagberg (2023) in their proposal for receding horizon planning, which was in fact also the reality in Soviet planning, where the quarterly plans – not the yearly plan – were orchestrating economic activity on the ground. However, there remains a fundamental tension between short-term plan fulfilment and long-term investment planning: Do we optimise for plan fulfilment in the current time period or at some point in the future? If it is the former, how can it be assured that projects receive their required resources in the present if they only begin to contribute towards plan fulfilment years down the road? At the same time, with optimising future plan fulfilment, there exists the serious risk of overinvestment and shortages in the present.

Any measures to deal with the dynamism of the economy will likely reintroduce the Soviet problem of supply uncertainty for enterprises, which will inevitable emerge with repeated plan revisions, which Kornai (1994) considered one of the most harmful aspects of the Soviet system. Already granted allocations could be withdrawn again at any point when it appears to be more optimal for the analytic engine. There would not be any contractual commitments, but only algorithmically determined truths, which would likely be in constant flux, as the choice here is between rigidity or uncertainty. On the one hand, one aspires for such dynamic adjustments to react to inevitable planning errors and unforeseeable changes in the economic system – a planned economy is damned to do so in the light of epistemic limitations and the contingency of the real world – while on the other hand, it is very likely that in the absence of contractual commitment the same tendency as observed in the Soviet Union will remerge, whereby enterprises hoarded critical components out of fear of shortages, which through their actions only worsened the situation for the whole economy by intensifying shortages or even creating them in the first place.

Here, the interests of individual producers are in contradiction with an optimal allocation of scarce resources within the wider economy because one cannot simply assume that all agents are rational actors, who will sacrifice their self-interest for the favour of the common good. As in the Soviet system, producers might not report the economic information truthfully or try to game the system. So it is not just the epistemic capacity to articulate these production functions that might be an obstacle to realising such an optimal planning framework, it is also the will of all participants to do so in good faith. For instance, by dramatically overstating the risk of a production stop or by inflating their input requirements, producers could accordingly increase the chance of receiving the required amount in time to the disadvantage of others, which could result in a highly dysfunctional spiral of inflating costs or distorted exigency if not inhibited. Through the comparison of different producers in the same industry an optimisation solver could route around inefficient production units but one would have be cautious here not to reintroduce some form of ‘mute compulsion' (Mau, 2023) with such measures.

Another critical problem is that one has to take into account outright malicious behaviour by agents, wishing to sabotage this planning procedure, whether this comes from within enterprises or from some foreign actor wishing to see the system fail is of lesser importance. What is the more pressing issue, however, is the fragility of such a model. This system has a single point of failure: if only a single constraint is violated the whole plan has to be recalculated. This does not even demand bad intentions and data poisoning attacks, but could result from banal errors in data input, such as when a digit is entered twice by mistake. All of this demands some sort of superintendence regarding data entry, which becomes even more problematic with small-scale economic activity. Well aware of the struggle to integrate this into such a framework, most optimal planners today are aligned to the Marxist orthodoxy of merging small businesses into larger entities, turning restaurants into canteens, organising trades within combines, merging factories into conglomerates and so on, to socialise production with the hope of reducing administrative redundancies and to ensure some control over their actions. But there also exist diseconomies of scale; big is not always beautiful. Spontaneous and small-scale economic activity fulfil critical functions in capitalist societies today. Will a socialist economy simply supplant them? If not, how can they be integrated, and their plans monitored? ²⁴

This brings with it an attendant group of questions regarding how much autonomy production units would truly have, not only in setting their output targets, choosing their inputs, and determining what to produce in the first place, but also in how one would be recognised as such. How are production units formed in the first place? Through which process are they accredited? Does any workplace have immediate access to all goods, or do there exist bureaucratic gates, wherein some superior coordinative entity must first unlock an item before it can be inserted into a production function? Can enterprises directly insert their requirements or does a superior have to confirm their actions? Regardless of his opposition to linear programming schemes, Stafford Beer’s understanding of a ‘viable system’ (Beer, 1981) would here suggest some sort of reconciliation between decentralisation and centralisation. Although the Soviet experience has proven that a certain degree of autonomy by the periphery is desirable, it would still have to be carefully balanced in such an algorithmic planning framework, as without any control instance in the form of democratically legitimised coordination boards for sectors, industries or regions, it would be simply too fragile and it would be way too easy to exploit such an optimisation algorithm to secure an unjust advantage in the allocation or outright sabotage the planning procedure by adding outlandish technical coefficients for which the algorithm would not find a feasible solution. Of course a technical outlier monitoring system for spotting these errors could be introduced, but there would still be the need for human oversight in form of some kind of institution, composed of bureaucrats or delegates, however one might call them.

Could coordination boards of delegates fulfil this function in a democratic way? Or are we not rather confronted with the threat of bureaucratisation and the re-emergence of a mid-level coordinator class in charge of the control over enterprises, resulting in a cat and mouse game of surveillance that is the breeding ground for nepotism and other forms of misuse of power? Even when these coordination boards would be democratically elected and could be recalled by majority decision, the officials would be in a position whereby well-established players would have much control over industries and could keep out others, who might wish to disrupt it by introducing creative destruction, or they could advocate inappropriate demands in support of their industry or region to the disadvantage of others. Creating another control instance to appeal against the decisions of a potentially corrupted coordination board might certainly be one possible solution, but here we once more encounter temporal issues. In keeping the economy running, there would be no time for long deliberative and consensus-oriented discussions, but economic urgency would demand swift decisions from these boards and other control instances, if one wished to avoid the economy grinding to a standstill. Likely, a fear of horizontalist paralysis would gradually erode any radical democratic structures of these institutions and might turn the whole apparatus into a bureaucratic machine, not differing much from the papacy of Soviet command planning.

A more elegant solution to this problem would be to come up with an incentive system which rewards producers to submit truthful information, thereby aligning particular interests with the common good. The Soviet system failed to articulate something like this. As stated above the target indicators were in many cases counterproductive, contradictory and the cause for many of the inefficiencies in the economy. So far, no formula has been discovered that cures these ills entirely. However, interesting research into rethinking such indicators has been undertaken more recently by David Laibman who developed the outline for a possible performance measure in a socialist economy (Laibman, 2015: 317–323), which includes a ‘comparison rate’ alongside qualitative and social performance measures, based on industry norms or enterprise history, which would incentivise worker groups to use resources more efficiently. By providing inflated information of input requirements, the performance measure of producers would be reduced. Although this is certainly an incentive to provide adequate information, it is doubtful whether this truly solves the need for central control over data entry and would completely eliminate the temptation to overstate the urgency of one’s own productive demands. False information, provided willingly or not, remains the Achilles’ heel of any such economic system. This problem, however, is not one that is exclusive to optimal planning realised through algorithms, but indeed to other planning frameworks based on deliberative negotiation as well. Only when enterprises are truly independent and allocation is achieved through a rationing by price one does not run into this problem, but then the invisible hand of the market is back in the driver’s seat again with all its devastating side effects and one can no longer speak of a rational administration of the economy in the light of ecological collapse, as the profitability of enterprises alone will determine the allocation of scarce resources.